Consultation Paper On Security Constrained Economic Despatch of Inter State Generating Stations pan India

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3 Consultation Paper On Security Constrained Economic Despatch of Inter State September

4 Contents Executive Summary 6 1 Existing Generation Scheduling Procedure in India Governance model Delicensing of generation Classification of generation Scheduling procedure 10 2 Scope of Optimisation Generation allocation procedure Spatial Distribution of Thermal Generation Based on Variable Costs Portfolio (Entitlement) Matrix Coordinated Decentralised Scheduling Regulatory interventions Jurisdiction 17 3 Objective Functions and Constraints for Security Constrained Economic Dispatch Load Forecast in decentralized scheduling Available Transmission Capability (ATC) Technical Minimum Ramp rate Input and Output database snapshots 20 4 Mathematical Model Linear Programming Duality in Linear Programming Linear Programming using Microsoft Excel Solver Linear Programming using GAMS 26 5 Key Inferences from the Optimization Results Optimization Results at a Glance Average Variable Cost and Scheduled Generation Marginal Cost Potential of Savings Marginal Cost and Demand Spinning Reserve Potential Savings and Flexibility 37 2

5 5.6. Summary of Equations Mechanism to utilize Savings 39 6 Maintaining Regional Secondary Reserves Mathematical model Procedure for maintaining regional spinning reserves 42 7 Framework for Implementation of Economic Despatch in India Gate Closure Spinning (Hot) and Non-Spinning (Cold) Reserves Ramping Requirements Inter-Regional Scheduling National Deviation Settlement (DSM) Pool Accounting and Settlement Optimization and Real Time Market (RTM) Way Forward 48 References 50 Annexe-I: Region wise Allocation Matrix of ISGS in Constituents pan India 51 Page 3 of 55

6 List of Figures Figure 1: Schematic of multilateral coordinated scheduling procedure Figure 2: Spatial Distribution of Thermal Generation in India based on VC Figure 3: Inter regional and international energy exchanges in India Figure 4: Matrix depicting the spread of allocations of regional ISGS Figure 5: Plant Load Factor (PLF) vs variable cost for fourty six (46) coal fired plants Figure 6: Highest and lowest Variable Cost of generation under RRAS Figure 7: Jurisdiction of Installed Thermal Capacity Figure 8: Snapshot of the sample regional summary Figure 9: Snapshot of the sample regional reserve summary Figure 10: Snapshot of sample plant wise optimization information Figure 11: Snapshot of actual generator schedules database Figure 12: Data flow in the optimization model Figure 13: Cumulative Installed Capacity of generation and Variable Cost per unit Figure 14: Movement of average variable cost with scheduled generation Figure 15: Marginal cost of generation and unconstrained MCP Figure 16: Block wise average Unconstrained MCP and Marginal Cost Figure 17: Ratio of Market Clearing Price to Marginal Cost Figure 18: Correlation between Marginal Cost and Unconstrained MCP Figure 19: Marginal cost of generation after optimization and base schedule Figure 20: Potential savings and Production Cost Figure 21: Average Production Cost and Potential Savings Figure 22: Block wise average Production Cost Vs Potential Savings Figure 23: Potential Savings as a percentage of Production Cost Figure 24: Variation of marginal cost and all India Demand Figure 25: Block wise average Marginal Cost and Demand Figure 26: Correlation of marginal cost and all India Demand Figure 27: Variation of Marginal Cost vs Spinning Reserve Figure 28: Block wise average Marginal Cost Vs Reserves Figure 29: Marginal Cost Vs Reserves Figure 30: Variation of Potential Savings and Spinning Reserves Figure 31: Block wise average Savings Vs Spinning Reserves Figure 32: Potential Savings Vs Spinning Reserves Figure 33: Schematic for Gate Closure Figure 34: Average Spinning (hot) and Non-Spinning (cold) Reserves Available Figure 35: Typical Ramp Rate for RRAS Providers Figure 36: Ramping Capability of Dadri (Thermal) Stage II Figure 37: Indian Electricity Market Roadmap for the Future Page 4 of 55

7 List of Tables Table 1: Summary of important numbers at a glance... 7 Table 2: Northern Region ISGS allocations pan-india Table 3: Eastern Region ISGS allocations pan-india Table 4: North Eastern Region ISGS allocations pan-india Table 5: Western Region ISGS allocations pan-india Table 6: Southern Region ISGS allocations pan-india Page 5 of 55

8 Executive Summary India has a federal structure of governance with 29 States and 7 Union Territories. Electricity is a Concurrent Subject with both the Central and State Governments having jurisdiction. Keeping in view the above, a decentralized, coordinated multilateral scheduling model has been adopted in the country in the mid-1990s. Electricity Act 2003 mandates that the Regional and State Load Despatch Centres (RLDCs/SLDCs) shall be responsible for optimum scheduling and despatch of electricity within the region, in accordance with the contracts entered into with the licensees or the generating companies operating in the region or the state, as the case may be [Sections 28(3)(a) and 32(2(a) respectively]. The state utilities schedule generation resources both from within the state and from inter-state generating stations (as per the entitlements) based on the forecasted demand. Multiple beneficiaries are allocated shares (within and across regions) in the inter-state generating stations (ISGS) and each state schedules as per its own requirement. This complex portfolio has led to a complex matrix with allocations to 29 states, 7 Union Territories as beneficiaries from around 150 Regional ISGS. While scheduling and despatch, each state is individually carrying out merit order despatch and thus a local level of optimization is already taking place. Depending on the requirement of the states, generation remains un-requisitioned in certain ISGS. This leads to a situation where, some beneficiaries are drawing costlier power from ISGS even while some fragmented leftover power in cheaper stations may remain un-despatched. Thus, there exists some scope for optimization by despatching cheaper stations. This aspect has become all the more evident with the implementation of Reserve Regulation Ancillary Services (RRAS). Given the federal structure with the coordinated multilateral scheduling model, there is a need for thin layer of optimization at the inter-state level duly factoring technical constraints such as technical minimum, maximum generation, transmission constraints, etc. Towards this end the Staff of the Commission has already come up with Discussion Papers on Real Time Energy Market and Re-designing Ancillary Services Mechanism in India. While the Real Time Energy Market is expected to help the stakeholders manage their energy portfolio closer to Real Time, the proposed framework of co-optimization of energy and ancillary services is intended for optimal utilisation of the generation resources at least cost. It will take some time for these frameworks to be fully operational and there is a scope for immediate savings in system costs through Security Constrained Economic Despatch of Inter State generating stations on pan India basis. It is in this context that a Consultation Paper on Security Constrained Economic Despatch of Inter State has been prepared which primarily focuses on Page 6 of 55

9 enhancing the scope of optimization of the Inter State thermal Generating Station schedules in India. The objective of the optimization exercise is to minimize the total generation cost while honouring the technical constraints of the power plants and the grid. Security Constrained Economic Dispatch of the shared power plants (ISGS) being despatched under the coordinated multilateral despatch mechanism in India is discussed in detail in this consultation paper. This framework would actually serve as a test bed / pilot project and act as a precursor to market based Ancillary Services Mechanism as proposed by the Staff of the Commission. A linear programming model is used to arrive at the optimal dispatch. The objective function and constraints underlying the model are described in detail. A software program developed for this purpose provided the real-time generator schedules and grid data as inputs to the model and has been in operation for over six months. Outputs and relevant data were archived, analysed and some observations are presented in this consultation paper. To harness the potential savings, a mechanism is also proposed in this consultation paper. Regulatory changes necessary for implementation of this optimisation mechanism are also discussed in detail. It is also pertinent to mention that Reserve Regulation Ancillary Services (RRAS) mechanism implemented in India from April 2016 has been a key enabler for this economic dispatch exercise. A methodology for maintaining secondary reserves at regional level is also discussed in this consultation paper. The summary of the analysis carried out is presented in Table 1 below. Table 1: Summary of important numbers at a glance Interstate Thermal Generation Plants 57 Nos Thermal Units 167 Nos Total Installed Capacity MW Range of Scheduled Power in a day MW MW Variable Cost range / kwh Marginal Price range 2-4 / kwh Weighted Average Variable Cost 1.89 / kwh Average Production Cost per day 186 Crores / day Average Potential Savings per day 2.4 Crores / day Production Cost that can be saved ~ 1.3 % Chapter-1 of the consultation paper discusses the background, evolution and the present framework of the generation scheduling procedure in India. Chapter-2 describes in detail the implications of the present framework and the scope for optimization. Importance of the Reserve Regulation Ancillary Services mechanism implemented in India, which paved way for the economic despatch exercise is brought out. Page 7 of 55

10 Chapter-3 discusses the objectives and constraints for the proposed optimization model. Chapter-4 is the mathematical representation of the descriptions in chapter-3. Chapter-5 provides the inferences from the six-month analysis. Chapter-6 dwells on the mathematical treatment for the reserve shutdown procedure and a way for maintaining secondary reserves. Chapter-7 discusses the changes in the regulatory framework needed to achieve further optimization. Page 8 of 55

11 1 Existing Generation Scheduling Procedure in India 1.1. Governance model India has a federal structure of governance with 29 States and 7 Union Territories. Electricity is a Concurrent Subject with both the Central and State Governments having jurisdiction. Keeping in view the above, a decentralized, multilateral coordinated scheduling model has been adopted in the country in the mid-1990s Delicensing of generation A paradigm shift has taken place in electricity market design post Electricity Act The Act mandates de-licensing of generation and open access in transmission [1]. Accordingly, the Central Electricity Regulatory Commission (CERC) introduced Open Access at the interstate level in May 2004 [2]. Along with these changes, the regional grids have also expanded and interconnected between and presently there is a well meshed All India electricity grid operating at a single frequency and also connected with the neighbouring countries Bhutan, Nepal, Bangladesh and Myanmar. These developments have facilitated competition in generation and introduced a vibrant wholesale electricity market Classification of generation Following these governance and regulatory models, in essence, there are broadly three types of generation plants in India (a) Shared central sector power plants and Ultra Mega Power Plants (UMPPs) in different parts of the country in which different states have entitlements. States are the beneficiaries which have the freedom and autonomy to requisition power from these generating plants. There are a few central sector power plants in which 100% entitlement is with only one state; (b) Independent Power Producers (IPPs) which have Power Purchase Agreements (PPA) with one or many states. In some cases, there are part merchant and part PPA contracts also, as generation is delicensed; and (c) State sector power plants in which the respective states have full share. In the types (a) and (b) mentioned above, generating stations in which two or more states have shares are defined as Interstate Generating Stations (ISGS). National Load Despatch Centre (NLDC) and the five Regional Load Despatch Centres (RLDCs) in India have the statutory responsibility of coordinating scheduling and dispatch of ISGS as stipulated in the CERC (Indian Electricity Grid Code), Regulations [3]. Scheduling co-ordination for type (c) plants and some type (b) plants mentioned above is done by the State Load Despatch Centres (SLDCs). The Scheduling Code in the grid code specifies the jurisdiction criteria for scheduling (whether RLDCs or SLDCs) and elaborates the procedure for scheduling of long term, medium term and short-term transactions (bilateral as well as collective). Page 9 of 55

12 1.4. Scheduling procedure ISGS declare their availability first which is followed by notification of entitlements to State Load Despatch Centres (SLDCs) by the RLDCs. SLDCs in turn give their requisition from ISGS to RLDCs. Revisions based on demand forecast and other factors are allowed to SLDCs. ISGS can also change their Declared Capability in case of constraints at the power plant. Considering all the above, injection schedule of the ISGS and drawal schedule of the states is collated. A schematic in this respect is given below as Figure-1. The scheduling process for ISGS adopted by RLDCs and NLDC hence can be broadly called a coordinated multilateral model [4]. Both hydro and thermal power ISGS plants are scheduled by the RLDCs/NLDC. Hydro and thermal have different set of associated constraints. The scope of this consultation paper is presently limited to optimization of schedules of the thermal power plants scheduled by RLDCs/NLDC, i.e., the ISGS stations for which the fixed and variable charges are known upfront through a regulated tariff mechanism. IPPs and merchant plants scheduled by the RLDCs/NLDC are not covered under the ambit of this consultation paper since there is no single notified fixed and variable charges for the entire plant output. Figure 1: Schematic of multilateral coordinated scheduling procedure Open Access to the transmission system provides an opportunity for optimization through bilateral contracts; thus a state having allocation from a cheaper central sector plant but facing less demand could still schedule its entitlement from the cheaper plant in full and sell the same through a bilateral contract to another state. This ensures that the cheaper plant is scheduled fully. Revision in schedules from Central sector plants at one hour notice also enables a proper response to real time system conditions. Page 10 of 55

13 2 Scope of Optimisation 2.1. Generation allocation procedure Broadly, shares in central sector generating stations to beneficiary states were allocated as per a pre-determined formula, popularly known as Gadgil Formula [5] since mid-1980s. The shares are allocated amongst the states of the region considering various factors such as population of the states and energy consumption during the previous five years. In case of joint ventures, the benefit was given in proportion of the equity contribution. After 2011, in accordance with the prevailing policy, power was procured by the Distribution Companies of states through tariff based competitive bidding. Above allocations led to a situation in which many ISGS sometimes have as many as fifteen (15) beneficiaries spread across India and many states have in their portfolio a share from more than 40 ISGS. Initially, such allocation to states was limited to states within a region. However, later, with strengthening of inter-regional connectivity and establishment of the National grid, such allocations spread across multiple regions Spatial Distribution of Thermal Generation Based on Variable Costs India has huge diversity in demand and generation. Resources in different regions of the country are also diverse. Coal is abundant in the Eastern and Central parts of the country while North Eastern and Northern Himalayan regions are rich in hydro resource. Similarly, several load centres are located near the coast. Northern, Southern and North Eastern regional ISGS allocations are mostly confined to their respective regions. The resource rich Western and Eastern regional ISGS allocations are present in all the regions. The spatial distribution of the thermal generation in the country is shown in the Figure-2 below. Installed Capacity (MW) 200<=MW< <=MW< <=MW< <=MW< <=MW< <=MW<4500 Figure 2: Spatial Distribution of Thermal Generation in India based on VC Page 11 of 55

14 Because of the diversity, different regions meet their peak demand at different time instants. Regional demand diversity factor is the ratio of the sum of the individual maximum demands of the regions of the system to the maximum demand of the grid. The value of the demand diversity factor at present is around 1.04 for the Indian grid. A huge and increasing transmission system is catering to the power transfer needs. Network security is assessed in the form of Available Transfer Capability (ATC) limits for the purpose of scheduling the resulting inter regional and inter-state flows. Around 700 million units of energy is exchanged daily between the regions as shown in the Figure-3. The regulated power plants have a multi-part tariff with two parts, namely fixed cost and variable cost. Each of the ISGS have a different generation tariff in accordance with the CERC (Terms and Conditions of Tariff) Regulations, 2009 as amended from time to time. Figure 3: Inter regional and international energy exchanges in India Page 12 of 55

15 2.3. Portfolio (Entitlement) Matrix This complex portfolio has led to a sparse entitlement matrix as depicted in Figure-4 which is a condensed indicative version of the entitlement matrix with 29 states, 7 Union Territories as beneficiaries from around 150 Regional ISGS which in effect implies about half a million contracts (approx. 150 x 36 x 96). As is evident from the figure, the allocations are fragmented. Figure 4: Matrix depicting the spread of allocations of regional ISGS Source: Data approximated and compiled from Regional Power Committee websites for representation Page 13 of 55

16 2.4. Coordinated Decentralised Scheduling A detailed entitlement matrix with complete list of ISGS and constituents is given as Annexe- I. Lot of interesting facts can be derived from this block diagonal matrix. The main advantages of this decentralised allocation matrix are - i. Risk mitigation for the procuring constituent, as the risk of outage is spread across multiple generators. The multi generator portfolio spread across many regions will behave like a huge flexible machine (or a super battery) for the procuring constituents which offers a huge ramp rate and reduced outage. ii. Decentralised allocation provides diverse portfolio to the procurer constituent to have a variety of generators in its portfolio, old and new generators, costly and cheap generators, base load and peaking generators, etc. iii. Diversity of different kinds can be exploited by the procuring utility facilitated by an all India synchronous grid because of these allocations. iv. Block diagonal nature of the matrix indicates that most of the generation resources are allocated within the region, which is the case with generation deficit regions viz., Northern Region, Southern Region and North Eastern Region. Deviation from block diagonal matrix nature is in the case of Eastern Region and Western Region, which are generation surplus regions. In fact, the present optimisation procedure itself has become possible because there is an all India synchronous grid with adequate transmission. Otherwise, generation and resources would be stranded within the region and it would have become difficult to harness the diversity and load factor of different parts of the country. v. This allocation firms up the energy to the constituents, thereby reducing the volatility of the day ahead market prices and spot markets to some extent. vi. Similarly, this decentralised allocation provides payment security to the generator, as the risk of non-payment is also spread. vii. In case of any non-requisition from the allocation, the generator has the option to enter the day ahead market, thus minimising any loss it might incur. On the other hand, there is a flip side to this decentralised scheduling as i. An elaborate scheduling process is required as mentioned in Chapter-1. ii. The contracts are inflexible, meaning quick transfer of allocations based on different needs of the states in various seasons, forecast etc. is difficult. iii. It is difficult to track the scattered remains of unscheduled generation from these allocations (Un requisitioned Surplus or URS) and to use it for different purposes such as Frequency Control through Reserve Regulation Ancillary Services (RRAS), Optimisation, Secondary Control through AGC etc. For example, a hydro rich state having allocation in a pit-head station may not requisition power from the pit-head Page 14 of 55

17 station in high hydro season which results in possible reduction in schedules for such pit-head stations. Notwithstanding the above, the decentralized scheduling mechanism or coordinated multilateral model has still brought about large efficiencies in the system. This would be evident from Figure-5 below where the plant load factors or plf for fourty six (46) coal fired Central sector and UMPP plants having an aggregate capacity of GW has been plotted against the variable cost. The PLFs for plants having variable cost of 200 paise/kwh or below is of the order of 90% which falls to below 65% as the variable cost crosses 300 paise/kwh. This is mainly on account of merit order being followed in the decentralized scheduling process and the competition introduced on account of open access in inter state transmission system. In Existing System High PLF for Low Variable Cost gen. Merit Order to a major extent Decentralized Open Access Ancillary Services Optimization would further: Bring National Level Merit Order Bring Flexibility in Scheduling Aid in Maintenance of Reserves Aid in Reduction of Start Stops Figure 5: Plant Load Factor (PLF) vs variable cost for fourty six (46) coal fired plants Slow Tertiary Control is implemented in India through Reserve Regulation Ancillary Services (RRAS) from April Over the period of time it has been observed that some Unrequisitioned Surplus (URS) is being left over in the cheaper stations as can be seen from Figure-6. The leftover power after requisitions from the entitlements is being scheduled in the merit order of variable cost under RRAS when needed. The beneficiaries surrendering their entitlement in any station get the benefit of fixed charges refund. Hence, the RRAS mechanism has the inbuilt feature of a thin layer of centralized scheduling in merit order of variable charges of the power plants. For this reason, the present work also Page 15 of 55

18 builds upon the database of RRAS. As RRAS also does merit order scheduling, the present economic dispatch exercise pre-rras would have yielded more savings through optimization. Figure 6: Highest and lowest Variable Cost of generation under RRAS 2.5. Regulatory interventions Some efforts have been made at the policy and regulatory level as outlined below with the objective being to harness the cheaper generating resources. These are: i. The Central Electricity Regulatory Commission (CERC) in its order dating back to January 2010 (and subsequent orders in other petitions) directed that in case of any Un Requisitioned Surplus (URS) in any Central sector station, the other sets of beneficiaries in the same power station would be free to get this power scheduled to them by making a request to RLDCs. Such scheduling would be treated as a temporary re-allocation which meant that the state which had surrendered its entitlement got some refund of fixed charges from the state to which this power was scheduled. As RLDCs statutory responsibility under the Electricity Act 2003 was to schedule power in accordance with the contracts, a standing clearance from generators for such URS scheduling was suggested. ii. With intra state transmission remaining essentially congestion free, implementation of S no i) above was easy once the standing clearances were in place. However, the same philosophy when extended to inter regional URS posed serious problems, particularly when the interregional transmission corridors were congested. There have been a few instances Page 16 of 55

19 where token allocations (similar to a golden share) have been made from power plants in one region to beneficiaries in another region to capitalize on the benefits of the above URS order. iii. The Tariff Policy dated 28 th Jan 2016 stipulated that for better utilization of unrequisitioned capacity from power plants with regulated tariff under section 62, the procurers or beneficiaries would inform 24 hours in advance their intent of unrequisitioning along with quantum of power and duration. The generator would be free to sell such quantum in the market and the gains (difference in market price and fuel charge) would be split 50:50 between the generator and the beneficiary which has unrequisitioned and informed the generator. The impact of the above mechanisms has been mixed. This is because it made more sense for generators and the beneficiaries to maintain status quo rather than being proactive. For instance, beneficiaries would rarely inform their intent of under-requisitioning as this quantum served as a hedge against any contingency such as tripping of their generator. If there was inaction on their part, it was evident that this unrequisitioned quantum, if scheduled to other states as per the URS order would fetch them a fixed charge refund. Even if the URS was not scheduled to any other beneficiary by RLDCs, they knew that scheduling of the same quantum under RRAS by NLDC would still fetch them a fixed charge refund. Similarly, generators were already getting their fixed charges from the beneficiaries and thus, there was no incentive for making aggressive efforts to sell in the market. If NLDC scheduled the same under RRAS, they would get a 50 paise/kwh mark up. Another pertinent aspect is the interplay with the Deviation Settlement Mechanism (DSM) rates. With the rate at 178 paise/kwh for frequency remaining not below Hz, this provided enough scope for interplay with the scheduling process and all the above mechanism. So while the above mechanisms were brought in with the objective of maximizing efficiencies, a passive approach was the one which maximized payoffs for each player be it the generator or the beneficiaries Jurisdiction This consultation paper explores the scope for an optimal solution to minimize the total production cost from all the thermal Inter State Generating Stations (ISGS) whose tariff is regulated or adopted by CERC without violating grid security and honouring the existing decentralized generation scheduling procedure. Page 17 of 55

20 Figure 7: Jurisdiction of Installed Thermal Capacity The total All India installed thermal capacity is approx. 221 GW out of which about 130 GW (59%) lies in the State sector. Out of the balance 91 GW constituting the inter-state thermal generation, about 56 GW capacity (25%) falls under the ambit of Reserve Regulation Ancillary Services (RRAS) whose tariff is determined or adopted by CERC. It is this capacity under the RRAS mechanism, that is considered in the scope this optimization exercise. During the financial year , the total All India Thermal Generation was 954 BU. The contribution from the thermal generation under RRAS was 343 BU (about 36%). The weighted average variable charges of the plants under RRAS for the year works out to 1.99 per kwh. A mechanism to harness this scope in savings is also suggested in this consultation paper. Procedure for inclusion of regional secondary reserves mandated by CERC in the Indian Power System is also provided in Chapter-6. Page 18 of 55

21 3 Objective Functions and Constraints for Security Constrained Economic Dispatch The standard centralized unit commitment techniques or the entire output through a spot market are difficult to implement in the Indian case because of the coordinated multilateral scheduling model explained in Chapter-1 which is a deemed delivered physically model [8], [9]. Presently, unit commitment is being done as per the operating procedure of CERC. A mechanism of compensation for fall in efficiency, heat rate degradation, increased auxiliary and secondary fuel oil consumption due to part load operation and multiple start-stop of units is also in place. CERC has mandated to maintain a spinning reserve corresponding to the largest unit outage in each region. The major scope of this consultation paper is limited to the optimization part after the unit commitment has taken place at a day ahead level viz. minimization of only the variable cost or fuel charges. The present problem hence assumes that certain units of the generating plants are already connected to the system and a base case schedule of these plants exists for the day of operation Load Forecast in decentralized scheduling In practice, the schedule of each plant is divided into 96 time blocks of 15 minutes, for the day of operation. The sum of basecase schedules of generators of all regions is the combined generation requisition by all the states to meet a part of the forecasted demand through their entitlement portfolio from central sector plants. Hence the sum of optimal schedules of all regions for the time block under consideration should be equal to the sum of schedules in the base case, as a strict equality constraint. Put differently, the model adheres to the commitment and the schedule already in place to look for adjustments within the day-ahead schedule to enhance system economics Available Transmission Capability (ATC) Available Transmission Capability (ATC) derived considering the network security constraints is the hard limit for the flow of power between the regions. Base case schedule of generation for each region is prepared such that the resulting scheduled inter regional flows shall not exceed the ATC limit. This has to be honored in the case of preparation of optimal schedule also Technical Minimum The technical minimum (turn down level) for operation of thermal ISGS units as decided by CERC is 55% of Maximum Continuous Rating (MCR) loading. The maximum each thermal generating plant can be scheduled is the Declared Capability on bar (DC on bar) of each plant. Page 19 of 55

22 Generation has to be scheduled from the cheapest to costliest variable cost of the generator for minimising the total production cost. The variable cost data is collected from the respective websites of Regional Power Committees (RPCs) Ramp rate Every thermal plant has its own ramp up or ramp down limits. This ramping data in units of MW per 15 minutes is furnished by all the ISGS and is collated from the RPC websites. The optimal generator schedule should always honour the up and down ramp rate limits of each plant with respect to its previous schedule Input and Output database snapshots Figure 8 to Figure 10 indicate the various inputs and outputs for the optimization exercise. Figure 8: Snapshot of the sample regional summary Figure 9: Snapshot of the sample regional reserve summary Page 20 of 55

23 Figure 10: Snapshot of sample plant wise optimization information Page 21 of 55

24 Figure 11: Snapshot of actual generator schedules database Page 22 of 55

25 4 Mathematical Model A mathematical formulation was made using the objective and constraints mentioned in Chapter-3. Though this has been done time block by time block, the optimization formulation can be done in a consolidated basis for all the 96 time blocks. Minimise % #&' " # $ # o k = total number of Plants o Where " # is the variable per unit cost of the * +, Plant o $ # is the optimised scheduled power of the * +, Plant Subject to % #&' % #&' Ø $ # = - # Ø $ # (0" ) Ø $ # $ #,9#: Ø $ #,+ $ #,+;' + 54AB Ø $ #,+ $ #,+;' =4>? D1E2 54AB Ø 5 =, H$ #,I - #,I J I (-"KL= I MN" I ) o S -is the scheduled power o t -represents current time of execution o R -represents each of the regions viz., North, East, West, South and North East o ATC -is the Available Transmission Capability of each region R o SCHIR -is the Scheduled Net Interchange of the region R o $ #,9#: is the technical minimum for thermal power plants, considered 55% of 0" Page 23 of 55

26 Figure 12: Data flow in the optimization model The plant wise database for parameters mentioned in Chapter-3 are populated. The above mathematical model is solved using the linear programming technique. Solver add-in in Microsoft Excel has been used for deriving optimal schedule for the time block t under consideration. General Algebraic Modeling System (GAMS) language and powerful commercial solvers like IBM CPLEX accessed through GAMS, was also used to sample check the results derived through Solver [8], [9]. A schematic of the data flow in the optimization model is given in Figure-10. This model is in place at the National Load Despatch Centre (NLDC) for over six months. Program was executed every five (5) minutes. All the input and output data was archived. This output result was retrieved, analysed and the inferences are presented in Chapter Linear Programming Linear programming is often used to solve problems in operations research. Linear programming is used for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Linear functions are characterised by linear relations between the variables, viz., simple addition of variables or multiplication of a constant and a variable. An example of a linear function is: F (A, B, C) = 10 A + 20 B C A, B and C are decision variables. The variables are multiplied by constants 10, 20 and From the mathematical model given in Chapter-4, it can be seen that all the objective function, equality and inequality constraints are all linear. Hence, linear programming is sufficient to solve the problem. Page 24 of 55

27 LP problems are usually solved using the Simplex method developed by Dantzig in Advanced methods from numerical linear algebra have made it possible to solve LP problems with many decision variables and constraints. In the practical use of Linear Programming in power sector, Singapore Electricity Market Rules have the market clearing formulation based on LP. n18.pdf 4.2. Duality in Linear Programming Dual values are the basic form of sensitivity analysis in LP. The dual value measures the increase in the cost of the objective function per unit increase in the value of the variable. In the case of linear problems, the dual values remain constant over a range. The dual value for a variable is nonzero only when the variable s value is equal to its upper or lower bound at the optimal solution. The dual value for a constraint is nonzero only when the constraint is equal to its bound. This is called a binding constraint. The dual value for the equality constraint mentioned in Chapter-4 is measured as the increase in the production cost per MWh increase in the base case schedule. This dual value provides the marginal cost of all India generation schedule. Dual value for inequality constraint (when there is a binding constraint) of ATC margin in Chapter-4 is calculated as the incremental increase in production cost per MWh increase in the actual import of the region. This incremental cost is added to the marginal cost of all India generation schedule to extract the respective region wise marginal costs. Split in regional marginal costs hence represents periods of transmission congestion Linear Programming using Microsoft Excel Solver Microsoft Excel Solver uses the primal Simplex method to solve LP problems. However, this Simplex algorithm does not exploit sparsity in the model. It is limited to 200 decision variables. A practical tutorial on Solver is available at To use a solver, a model has to be built as shown in Chapter-4 consisting of decision variables, objective function, and constraints on the variables. Solver will find the optimal solution for the decision variables that will satisfy the constraints while optimizing (maximizing or minimizing) the objective. The decision variables for the model are worksheet cells containing values Solver can decide. The objective function is the cell containing the formula of the linear objective function. Solver can be set to minimise (for e.g. production cost) or maximise the value of the objective function by adjusting the values of the decision variable cells. Constraints are logical conditions on formula cells that must be satisfied (specified with <=, = or >= relations). Coding in a programming language such as Visual Basic (Macro) associated with Microsoft Excel can be used to automate the process to some extent. Page 25 of 55

28 The dual values are extracted from the Sensitivity Report of the Microsoft Excel Solver. Detailed notes can be obtained from the link Linear Programming using GAMS Linear Programming with GAMS needs a mathematical formulation as shown in Chapter-4. This math model can be translated into GAMS and can be solved using any of the solvers available with GAMS. The demo version of GAMS is available at It includes all features and solvers, but the size of the models that can be solved with the demo version is limited (for LPs: 300 constraints, 300 variables, 2000 non-zeros). GAMS example for converting mathematical model to GAMS language is available at In GAMS, the command equation.m provides the marginal value for equation. Marginal is reset to a new value when a model containing the equation is solved. The marginal value for an equation is also known as the shadow price for the equation. Page 26 of 55

29 5 Key Inferences from the Optimization Results 5.1. Optimization Results at a Glance The data analysed was for 57 thermal plants of approximately 55 GW total capacity from September 2017 to March 2018 and the summary is given below. Interstate Thermal Generation Plants Thermal Units Total Installed Capacity Range of Scheduled Power in a day Variable Cost range Marginal Price range Weighted Average Variable Cost Average Production Cost per day Average Potential Savings per day 57 Nos 167 Nos MW MW MW / kwh 2-4 / kwh 1.89 / kwh Production Cost that can be saved ~ 1.3 % 186 Crores / day 2.4 Crores / day The variable cost of these plants ranges from 1 to 8 /kwh. The plants are of different sizes and the weighted average variable cost is around 1.89 /kwh. A representation of the cumulative installed capacity of the plants in the order of increasing variable cost is given as Figure-13. Page 27 of 55

30 Figure 13: Cumulative Installed Capacity of generation and Variable Cost per unit The average production cost per day was approx. 186 Crores. Potential savings (difference between base case and optimal results) were estimated around 2.4 Crores on an average, which is approximately 1.3% of the total average production cost. Low marginal cost was observed during off-peak hours and high costs were observed during peak. There was a transition in marginal prices associated with demand ramp. The dual value for the equality constraint mentioned in Chapter-4 is measured as the increase in the production cost per MWh increase in the base case schedule. This dual value provides the marginal cost of all India generation schedule. Dual value for inequality constraint (when there is a binding constraint) of ATC margin in Chapter-4 is calculated as the incremental increase in production cost per MWh increase in the actual import of the region. Split in regional marginal costs hence represents periods of transmission congestion Average Variable Cost and Scheduled Generation As expected, the production cost increases linearly with the quantum of scheduled generation. The average variable cost, on the other hand, increases in a polynomial fashion with quantum of scheduled generation as shown in the scatter plot Figure-14. Page 28 of 55

31 Figure 14: Movement of average variable cost with scheduled generation 5.3. Marginal Cost The marginal cost of generation obtained from the linear programming results of the mathematical model and the unconstrained Market Clearing Price (UMCP) discovered in the Day Ahead Market through Power Exchange have a great similarity in the diurnal pattern as shown in Figure-15; Figure-16 shows the block wise correlation between UMCP and marginal cost. It may be inferred from this that the generators bidding in the power exchange bid at least at the marginal cost to be on the safer side in order to avoid incurring losses. The MCP is the value of the power as decided by the consumer (both fixed and variable costs included) whereas marginal price is the cost of the next unit of electricity as per the merit order stack of variable cost. Page 29 of 55

32 Figure 15: Marginal cost of generation and unconstrained MCP Figure 16: Block wise average Unconstrained MCP and Marginal Cost The ratio of MCP to marginal cost is usually greater than one as shown in Figure-17. In the off peak hours, it can be observed that the ratio is less than one implying units are on bar despite incurring loss for earning profit in the rest of the day. Page 30 of 55

33 Figure 17: Ratio of Market Clearing Price to Marginal Cost A good correlation can also be observed between the price discovered in the day ahead power exchange price and the marginal cost of the generation dispatched in real time as shown in Figure-18. Figure 18: Correlation between Marginal Cost and Unconstrained MCP A high correlation was observed between the marginal cost of generation after optimisation and the scheduled generation before optimisation as shown in Figure-19, indicating the present schedule is broadly following the merit order. When the power requirement was low, only the least variable cost units were dispatched and high variable cost units had to be dispatched only when the requirement was high. Page 31 of 55

34 Figure 19: Marginal cost of generation after optimization and base schedule 5.4. Potential of Savings It was observed that the potential of savings was high when the production cost was low as shown in Figure-20. This also corroborates with Figure-19, where cheaper units were used to meet the low demand, and some units must be operating at part load and there was some scope for savings because of fragmented nature of allocations. Compensation for loss in heat rate and increase in auxiliary consumption is not factored at present in the formulation. So, the potential savings are slightly over estimated. Figure 20: Potential savings and Production Cost Trend indicates that the possible savings because of optimisation were the highest during off peak hours as shown in Figure-21. These off peak hours are also the low demand periods and Page 32 of 55

35 the low production cost periods. Figure-22 brings out the correlation between block wise average production cost versus potential savings. Figure 21: Average Production Cost and Potential Savings Figure 22: Block wise average Production Cost Vs Potential Savings As shown in Figure-23, the potential savings as a percentage of production cost is around 1.3%. This low savings percentage also indicates that in the present decentralised multilateral coordinated scheduling model for the ISGS, generation scheduling is being done in the merit order of variable cost of generation to a large extent. However, the fragmented nature of allocations and the decentralized scheduling coupled with the inflexibility of bilateral contracts as mentioned in earlier Chapters still leaves scope for incremental optimization and generating savings. Page 33 of 55

36 Figure 23: Potential Savings as a percentage of Production Cost 5.5. Marginal Cost and Demand The marginal cost of generation has a clear pattern in following the all India demand as shown in Figure-24. During periods of high demand, the production cost and the marginal cost are also high. Figure-25 brings out the correlation between block wise average marginal cost and demand met. MC Figure 24: Variation of marginal cost and all India Demand Page 34 of 55

37 Figure 25: Block wise average Marginal Cost and Demand A very good correlation can also be observed between the marginal cost and the all India demand. A scatter plot representing the same is given as Figure-26. Figure 26: Correlation of marginal cost and all India Demand 5.6. Spinning Reserve Similarly, marginal cost was high when the up regulation spinning reserve quantum was not available as shown in Figures This is also logically in sync with high marginal cost during peak demand and no new unit commitment. During periods of high demand, the marginal cost of generation is high and the generation dispatch along with the total production cost is also high. This leads to a case where almost all the on bar generation is dispatched to meet the demand. Only the units on reserve shutdown will not be dispatched. The reserves in this case are close to nil and hence the marginal cost shoots up. Chapter-6 gives some mathematical treatment to this problem. On the other hand, during off peak periods, reserve availability will improve and marginal prices Page 35 of 55

38 decrease. Figure-26 brings the correlation between block wise average marginal cost and reserves. MC Figure 27: Variation of Marginal Cost vs Spinning Reserve Figure 28: Block wise average Marginal Cost Vs Reserves Page 36 of 55

39 Figure 29: Marginal Cost Vs Reserves 5.5. Potential Savings and Flexibility A high correlation was observed between potential savings and spinning reserves. This indicates that for meeting the diversity in off peak and peak demand in the same day, some units were operated at technical minimum of 55%. The turn down level of thermal plants before the fourth amendment to IEGC was 70% [11]. Also, by changing the technical minimum level in simulation from 70% to 55%, it was observed that flexible power plants provide higher savings potential when the scope for optimization spans across the entire day. Figure 30: Variation of Potential Savings and Spinning Reserves Page 37 of 55

40 Figure 31: Block wise average Savings Vs Spinning Reserves Figure 32: Potential Savings Vs Spinning Reserves Page 38 of 55

41 5.6. Summary of Equations The below is a table of equations presented in this chapter for block wise average quantities, expressing the relationship pattern observed between various parameters and the confidence level. Table-2: Summary of equations expressing relation between various parameters Fig.No. x y Equation R 2 14 Marginal Unconstrained y = -7E-06x x x Cost Market clearing Price 20 Production Potential Savings y = 5E-06x x x Cost 23 Demand Marginal Cost y = 4E-12x 3-2E-06x x Spinning Marginal Cost y = -5E-10x 3 + 7E-06x x Reserve 29 Spinning Reserves Potential Savings y = -3E-11x 3 + 2E-07x x Mechanism to utilize Savings There is a need of a mechanism to harness the savings potential mentioned in this Chapter. To harness these savings, a mechanism similar to the CERC Ancillary Services regulations is proposed [13]. RLDCs can proceed with the regular scheduling process like present as mentioned in Chapter-1 and 2. An optimization software at NLDC/RLDC can look at the (T+2)th time block at the beginning of every (T)th time block and produce the optimal schedule of (T+2)th block for every generator considering ramp rate, variable cost, generator limits and transmission margin. NLDC/RLDC can then centrally advise for Up and Down regulation on every generator based on the optimization software results. To avoid Up and Down changes for small potential savings, a dead band of 10 MW can also be introduced for incremental changes over the existing schedule similar to the RRAS regulation. Gate closure time has to be implemented. This will result in a thin centralized layer of optimization over the present decentralized scheduling. The optimization can also be extended to tertiary reserves kick in and a cooptimization of energy, ancillary services and reserves is also possible. Chapter-7 discusses in detail the regulatory and procedural changes needed to implement the optimization exercise. As a part of understanding the applications of optimization in power sector, it is felt that only the right combination of optimization applications and regulatory interventions will bring in best results. In doing this, the existing practices and market design have to be considered and suitably modified, for the proposed applications to be practical. Page 39 of 55

42 6 Maintaining Regional Secondary Reserves Importance of spinning reserves in the system operation was reiterated by Hon ble CERC in several documents and orders, including the Order dated 13 th October 2015 on Roadmap to Operationalise Spinning Reserves and the Report of the Committee on Spinning Reserves [14]. Region wise Secondary Reserves were earmarked by the Hon ble Commission corresponding to the largest unit outage in each region. Secondary reserves were mandated to be operationalised through Automatic Generation Control (AGC). Secondary Reserves Region wise Region Reserve (in MW) North 800 MW East 660 MW West 800 MW South 1000 MW North East 363 MW Total 3623 MW Note: The quantum indicated above might change in future with increasing Renewable Energy (RE) penetration as per the Report of the Committee on Spinning Reserves The scope of this consultation paper hitherto (till chapter 5) is limited to the optimization part after the unit commitment has taken place at a day ahead level. Chapter 6 touches upon the unit commitment aspect with regards to maintaining regional secondary reserves. Regional Secondary Reserve is calculated mathematically as Regional Secondary Reserve = 5 =, H0" $ #,I J I o $ # is the optimised scheduled power of the * +, Plant o R -represents each of the regions viz., North, East, West, South and North East The mandated regional secondary reserve quantum has to be maintained in each region, which can be operationalised through secondary control. For this, the base case schedule preparation mentioned in Chapter-1 shall include regional reserve requirement while preparing the final schedule. The proposal is that, in addition to the scheduling procedure taking place mentioned in Chapter-1, an extra layer of optimisation for reserves can be run say at each RLDC. Page 40 of 55

43 6.1. Mathematical model The new constraint that would come under the optimisation formulation for preparing the base case schedule for all the 96 time blocks is given below: 5 =, H0" $ #,I J I > =BP*124Q =BRB5SB =BT@*5B>B2A The consolidated set of equations which can be used for preparing the region wise final schedules for the next day are given below UV +&' % #&' Minimise " # $ # o k = total number of Plants o t -represents each of the 96 time blocks of the day o Where " # is the variable per unit cost of the * +, Plant o $ # is the optimised scheduled power of the * +, Plant Subject to A (1,96) % #&' % #&' Ø 5 =, $ #,+,I = - #,+,I Ø $ #,+ (0" ) Ø $ #,+ $ #,9#: Ø $ #,+ $ #,+;' + 54AB Ø $ #,+ $ #,+;' =4>? D1E2 54AB Ø -"KL= I,+ < MN" I,+ Ø 5 =, H0" $ #,+,I J I o S -is the scheduled power > =BP*124Q =BRB5SB =BT@*5B>B2A o R -represents each of the regions viz., North, East, West, South and North East o $ #,9#: is the technical minimum for thermal power plants, considered 55% of 0" o =BP*124Q =BRB5SB =BT@*5B>B2A for each region is as provided in the table above. o ATC -is the Available Transmission Capability of each region R o SCHIR -is the Scheduled Interchange of the region R o $ #,9#: is the technical minimum for thermal power plants, considered 55% of 0" The output expected for each region would be the optimised schedule after including regional reserves for all the 96 time blocks. By product of the optimisation process can also indicate which units have to be started in case of shortfall of reserves. The typical start up time of the Page 41 of 55

44 thermal units is around 6 to 8 hours. The shortfall in reserves is typically observed in the peak hours, during morning and evening peak. Hence the start-up instruction can go to the unit by 2300 hrs of the previous day at the latest Procedure for maintaining regional spinning reserves Generally, when many machines are on bar and there is margin from DC on bar available in the running machines, then the cost of keeping regional spinning reserve is minimal. However, when the constraint of reserve is not satisfied, then the next costly unit is to be necessarily brought on bar and generation on a next cheaper unit has to be reduced. The cost of keeping the reserve is the differential of the variable cost of the units plus the start-up cost. The below procedure will help in economically bringing the units on bar from Reserve Shut Down in case of shortfall of reserves: i. In case of reserve shortfall, the output of the program provides the Reserve deficiency (shortfall of the regional reserve from the mandated requirement) as a slack. ii. Next, the input reserve requirement of the program is adjusted by the slack to make the reserve requirement of that region equal to the available reserve, an equality constraint. iii. The program then points to the marginal cost unit by using the relevant dual variable extracted from the program. iv. These marginal cost units have to be brought on bar in the program and the program has to be re-run iteratively, till the slack (reserve shortfall) becomes zero. v. The final output would be the optimised schedule of each region after including regional reserves for all the 96 time blocks. For example, the program output may suggest that one 800 MW unit of Kudgi has to be started till technical minimum (440 MW) to meet the reserve requirement of Southern Region, and the next costlier unit in Southern Region has to be backed down, say Simhadri stg-i by 440 MW. Cost for maintaining this reserve is the differential of the variable cost of the units, 3.75 /kwh minus 2.45 /kwh, i.e., 1.3 /kwh excluding the start-up cost. Page 42 of 55

45 7 Framework for Implementation of Economic Despatch in India The coordinated multilateral scheduling model prevailing in the country has been deliberated earlier in this consultation paper. With this decentralized scheduling and despatch model in place, a thin layer of centralized optimization utilizing the generation resources available at the inter-state level can mop up residual cheaper generation resulting in overall savings. Implementation of energy and reserves optimization is feasible in the Indian scenario with some changes in the existing framework. Some of these changes are deliberated in the following sections. 7.1 Gate Closure In the present framework, revision of schedules by the market participants is permitted and as per the Scheduling and Despatch Code under the Indian Electricity Grid Code (IEGC), the schedules can be revised by giving a notice of four (4) time blocks (each of 15-minutes). Given the large number of participants, there are requests for revisions in schedule on an almost continuous basis. This also poses problems in real time assessment of the available hot and cold reserves available in the system. In order to implement the proposed optimization process at the Regional/National level, it is necessary that gate-closure be introduced. Conceptually, prior to gate closure, the flexibility of revising the schedules is with the market participants and post gate closure, the system operators take over and prepare for the pre-determined delivery period. For the Indian context, a suggested schematic for implementation of gate closure is shown in Figure 33 below. Real Time Market (RTM) RTM Clearing Optimization Reserves Activation, Ancillary Preparation Time for Despatch Secondary & Fast Tertiary Control Action Delivery Period Gate Closure at 1000 Hrs for delivery period Final schedules communicated to RLDCs Figure 33: Schematic for Gate Closure Page 43 of 55

46 The introduction of gate closure will bring in more certainty of despatch specially in terms of reserves requirement & activation thereof. 7.2 Spinning (Hot) and Non-Spinning (Cold) Reserves A snapshot of typical spinning (hot) and non-spinning (cold) reserves available on a particular day are shown in Figure-34 below Typidal Daily Spinning & Non-Spinning Reserves Average of All India Total UP REGULATION Average of All India Cold Reserve MW :00 0:55 1:50 2:45 3:40 4:35 5:30 6:25 7:20 8:15 9:10 10:05 11:00 11:55 12:50 13:45 14:40 15:35 16:30 17:25 18:20 19:15 20:10 21:05 22:00 22:55 23:50 Time Block (5min) ---> Figure 34: Average Spinning (hot) and Non-Spinning (cold) Reserves Available It is evident from the figure above that while some spinning (hot) reserve is available during other hours of the day, the hot reserves are depleted during the evening peak hours. Cold reserves, comprising of generating units available but not synchronized, need to be brought on bar under such circumstances. As the time required for thermal units to come on bar is high, such units also need to be kept running at technical minimum levels during the other hours of the day. This is effect also means out of merit generation despatch with the objective of maintaining adequate reserves for reliable and secure operation of the grid. CERC has given a roadmap for maintaining reserves vide Suo-Motu Order dated 13 th October From an enforcement perspective, a mandate for reserves in the appropriate Regulations is needed for maintaining reserves and calling cold reserves into service as and when needed. Incorporation of reserves can be done in the Security Constrained Economic Despatch Algorithm as explained in Chapter-6 above. Page 44 of 55

47 7.3 Ramping Requirements Regulation 7(4) of the CEA (Technical Standards for Construction of Electrical Plants and Electrical Lines) Regulations 2010 specifies the generating units shall have a minimum rate of loading or unloading of 3% per minute above the control load (i.e., 50% MCR). The Indian Electricity Grid Code (IEGC) provides that a minimum of 1% ramp rate shall be provided by the generators. The ramp rates declared by the RRAS providers are lesser than 1% presently (sample shown in Figure-35 below). Figure 35: Typical Ramp Rate for RRAS Providers In this context, recently tests have been conducted at the Dadri (Thermal) Stage II Station to check the ramping capability. The test has demonstrated that 3% is achievable as shown in Figure-36 below. Figure 36: Ramping Capability of Dadri (Thermal) Stage II Page 45 of 55

48 7.4 Inter-Regional Scheduling Presently, corridor wise path specific scheduling is carried out by the Regional Load Despatch Centres (RLDCs). Inter-regional schedules are also being reconciled by the neighbouring regions. Deviations from schedules in the actual power flows are computed based on the energy meter readings and are accounted for in the regional energy accounts. Optimization of energy and reserves may lead to where regional injection which have an impact on the inter-regional corridor schedules. For example, schedules of pit-head cheaper stations in western region may increase and some costly load centre stations may be asked to reduce in the northern & southern regions. Power flows according to the laws of physics and corridor/path wise scheduling methodology presently being used would only be distributing the scheduled flows heuristically. Hence, for implementation of the optimization process, it is necessary to change the scheduling methodology from corridor wise scheduling to net-injection/net-drawal for each region. This would require amendments to the Scheduling and Despatch Code under the Indian Electricity Grid Code (IEGC). 7.5 National Deviation Settlement (DSM) Pool It has already been mentioned above that corridor wise path specific scheduling is carried out by the Regional Load Despatch Centres (RLDCs). Any deviations from schedules are accounted and settled for each corridor between the neighbouring regions. Payments are made from one regional DSM pool to the other DSM pool. Scheduled inter-regional power flows and the settlement between DSM pools sometimes also leads to circular flow of funds. As proposed above, the methodology for inter-regional scheduling needs to be changed from corridor wise scheduling to a net-injection/net-drawal basis for each region. With this implementation, there is a need for implementation of a National Deviation Settlement (DSM) Pool. All Regional DSM pools would then interact with the National DSM Pool only. Optimization would result in changes in injection/drawal schedules for each region and there would be a need for pay-in/pay-out from the DSM pool accounts for the incremental changes in schedules (up/down). Payment for reserves called upon through ancillary services (both RRAS and proposed FRAS) and AGC are also being made from the surpluses available in the respective Regional DSM Pool accounts. In case adequate surplus funds are not available to meet these payment requirements, then, surplus funds from other regions are transferred to support these requirements. Implementation of the National DSM Pool would reduce the multiple inter-pool transactions as now the Regional DSM Pool would interact only with the National DSM Pool. This would also streamline the entire settlement process. The optimization exercise would involve payments to and from this National DSM Pool as stated in the next section. Page 46 of 55

49 7.6 Accounting and Settlement As per the present mechanism, the generators receive their variable charges based on the schedules issued by the concerned RLDC. Optimization would result in incremental changes in the existing schedules and these would need to be settled. The following methodology is proposed for settlement of the incremental changes in schedules as a consequence of optimization: (a) The variable charges declared by the generators for the purpose of ancillary services shall be considered in the optimization process. (b) Schedules of the states/beneficiaries shall not be changed and the beneficiaries would continue to pay the charges for the scheduled energy directly to the generator. (c) For any increment in the injection schedule of a generator due to optimization, the generator would be paid the variable charges from the National DSM Pool. (d) For any decrement in the schedule of a generator due to optimization, the generator shall refund the variable charges to the National DSM pool. (e) It is anticipated that optimization would result in some overall savings and there would be no deficit in the pools for payment of variable charges. (f) The incremental changes in schedules on account of optimization shall not be considered for incentive computations for the generating stations. (g) A nominal mark-up, if any, could be suitably decided by the CERC. From the above study, it is also observed that if a mark-up of more than paisa per unit is given to the generators, then the total savings achieved in the SCED process are exhausted and would not be self-sustaining. 7.7 Optimization and Real Time Market (RTM) The proposed optimization mechanism utilizes the variable charges declared by the generator upfront. An alternate to this could be that the generators submit bids which are then considered in the optimization process. This would also expand the ambit of the above mentioned optimization to include IPPs/Merchant Power Plants being scheduled by the RLDCs/NLDC. A real time market, which is analogous to the hour-ahead market, is also being deliberated for implementation. This market segment provides another opportunity to the market participants to balance their portfolios closer to the time of operation. However, given the coordinated multilateral model adopted in the country, there may still be some opportunity to optimize the schedules, however small it may be. Moreover, optimization would also determine the required reserve quantum. Hence, the real time market and optimization efforts are complementary. Page 47 of 55

50 8. Way Forward In this consultation paper, optimization of schedules of central sector power plants was presented to minimize the total production cost. Existing procedures and relevant constraints were discussed in detail. The entire optimization exercise proposed requires some changes in regulations and procedures, i.e., the savings estimated herein can be achieved with altering some of the current rules. An application was developed and data obtained by running the application on real time data was analyzed and inferences were presented. The key inference is that some scope for optimization exists and more so in the off-peak hours. The overall scope for optimization is small, to the tune of 1.3% of the total production cost. To harness these savings, suitable provisions in the Regulations are required. A method for optimal procurement of regional secondary reserves is also provided. Ministry of Power, Government of India has issued guidelines for Flexibility in Generation and Scheduling of Thermal Power Stations with the objective of utilizing any un-despatched surplus in existing cheaper generating stations by way of flexibility in scheduling of generation and cause economy and efficiency leading to overall reduction in production costs. The proposed application of optimization techniques through the Security Constrained Economic Despatch (SCED) at the inter-state level offers an elegant mechanism to achieve the policy objectives laid down by the Ministry of Power, Government of India. Implementation of optimization techniques in the Indian Power System would also offer the following advantages: (a) National level merit order amongst interstate generating stations can be achieved (b) Flexibility in scheduling of generation on a country-wide basis can be facilitated (c) Security constraints can be modeled in the optimization algorithm (d) Schedules for States and the underlying settlement systems remain undisturbed (e) Algorithmic based scientific approach using established optimization techniques is used which has higher acceptability by the stakeholders (f) Optimization in conjunction with ancillary services can reduce the number of start/stops for generators (g) Maintaining secondary reserves at Regional Level Under the Security Constrained Economic Despatch (SCED) mechanism where pool-based settlement has been suggested, the benefits shall be shared by a larger set of beneficiaries. This is in effect means sharing of gains by all load serving entities / distribution utilities across the country as a whole in terms of enhanced system reliability. Page 48 of 55

51 Electricity Market was introduced in the country with the introduction of open access in interstate transmission in the country in May The Indian Electricity Market has come a long way since then with a number of policy and regulatory interventions to take the market forward. A market-based approach also facilitates competition and a larger participation. The present state of the Indian Electricity Market and the proposed roadmap for the future is shown in the Figure-37 below. Market Maturity Individual Buyer /Seller Gadgil Formula Multiple buyers - sellers Spot Markets on Exchanges OTC Markets Indian Electricity Market Competitive Bidding based Procurement Integration from Regional to National Grid RE Auction markets Ancillary Services RRAS Gate Closure FRAS Improved Frequency Profile Supply Adequacy SCED- Coopt Ancillary Real Time Market Scheduling & Despatch - Allocation Matrix ~150 Plants (Inter-State) 36 States/UTs beneficiaries Approx. half a Million contracts/day Financial instruments Till 1990s 2003 onwards 2011 onwards 2016 onwards Present 2018 Future 25 Figure 37: Indian Electricity Market Roadmap for the Future Page 49 of 55

52 References [1] Electricity Act, 2003, Govt. of India, Jun-2003 [2] Central Electricity Regulatory Commission (Open Access in Inter-state Transmission) Regulations, [online] Incorporated.pdf [3] Central Electricity Regulatory Commission (Indian Electricity Grid Code) Regulations, [4] F. F. Wu and P. Varaiya, Co-ordinated multilateral trades for electric power networks, theory and implementation, Intl. J. of Electrical Power & Energy Systems, vol. 21, no. 2, pp , Feb [5] Press release - Allocation of Power from Central Pool, Ministry of Power, Government of India. [online] [6] Allen J. Wood and Bruce F, Wollenberg, Power Generation, Operation, and Control, Chapter -3, John Wiley & Sons, New York, [7] Grainger, John J., and William D. Stevenson. Power System Analysis. New York: McGraw- Hill, [8] GAMS Development Corporation. General Algebraic Modeling System (GAMS) Release Washington, DC, USA, [9] Microsoft Excel Solver add-in. [online] 5d1a388f-079d-43ac-a7eb-f63e [10] Central Electricity Regulatory Commission (Ancillary Services Operations) Regulations, 2015 [online] [11] Central Electricity Regulatory Commission (Indian Electricity Grid Code) (Fourth Amendment) Regulations, [online] [12] CERC Order on Roadmap to operationalise Reserves in the country, 13th October 2015 [online] Page 50 of 55

53 Annexe-I: Region wise Allocation Matrix of ISGS in Constituents pan India Table 2: Northern Region ISGS allocations pan-india Constituents à North East West South North East Northern Region ISGS Page 51 of 55

54 Table 3: Eastern Region ISGS allocations pan-india Constituents à North East West South North East Eastern Region ISGS Page 52 of 55

55 Table 4: North Eastern Region ISGS allocations pan-india Constituents à North East West South North East North East ISGS Page 53 of 55

56 Table 5: Western Region ISGS allocations pan-india Constituents à North East West South North East Western Region ISGS Page 54 of 55

57 Table 6: Southern Region ISGS allocations pan-india Constituents à North East West South North East Southern Region ISGS Page 55 of 55