Cost/benefit analysis of utility management systems implementation

Transcription

1 Cost/benefit analysis of utility management systems implementation by Juliana Katić and Nenad Katić, Schneider-Electric-DMS, Novi Sad Smart grid software solutions are one of the key topics in the power utilities world nowadays. A special focus is put on investments in utility management systems (UMS). In this paper, cost/benefit analysis of investment in UMS was conducted, from a revenue and an environmental point of view. Real market cost data and information about different possible benefits from an actual utility s experiences were used. Furthermore, different cases were analysed utilities of different sizes, different market cases and UMSs of different scopes in order to assure the accuracy and sensibility of the analysis. Over the last ten years, smart systems for grid management have expanded rapidly. Ideal of the unique smart grid management system is now becoming more and more realistic. System control and data acquisition (Scada), distribution and transmission management Systems (DMS, EMS), and smart meter control systems (AMI) were all traditionally separated systems, now they are being integrated in the unique smart UMS. These systems provide support for optimal use of energy resources, management of the renewable energy resources, loss reduction, complying with strict regulator policies, reliable supply to the end customers, outage management as well as many other, crucial issues in the modern energy efficient world. Areas for optimisation Firstly, a theoretical analysis of the distribution utility problems and potential solutions with UMS software has been done [9]. Main problems that utilities face every day have been evaluated, in order to detect potential areas for improvement using UMS software [9]. Nowadays, distribution utilities face the following main problems: High expenses for every day, normal operation: - Expenses for maintenance and other repair work. - Expenses due to power losses. - Expenses for the compensation of the non-delivered energy. Expensive investments in the network development (network expansion with new capacities and automation). Poor supply quality and voltage profiles, customer claims. Strict regulator policies regarding key performance indices (KPIs). Afterwards, different UMS software functionalities have been analysed and their potential impact on the listed problems: High expenses for every day, normal operation: UMS provides support for everyday commanding by smart selection of manoeuvres, total number of operations is reduced, prolonging the lifecycle of the switchgear equipment. Furthermore, UMS Optimisation functions [2,6] provide support for the optimal configuration of the network by using existing capacities: by transferring the network from its current to the more efficient (optimal) radial configuration, significant loss reduction can be achieved. Smart applications for outage management in medium voltage and low voltage network reduce the outage time, which means direct savings for the utility (more delivered energy and less penalties for the non-delivered energy). Expensive investments in the network development: UMS provides tools for the smart planning of the network development. It determines the optimal place for the new network equipment and automation, supporting the user in the smart and efficient planning of the network reinforcement [10]. Poor supply quality and voltage profiles, customer claims: by optimising the network operation in real-time, the quality of the supply and voltage profiles are significantly improved. This is also the only improvement which is directly experienced by the end customers in their home, providing thus the added value to the utility. Beside the lower number of complaints, the overall customer satisfaction is the important factor for the utilities on the competitive market. Strict regulator policies regarding key performance indices: by optimising the daily network operation and reducing the power losses, key performance indices are directly improved. Beside the improvement itself, UMS also provides the tools for the automatic calculation of the KPIs and generation of reports which can be automatically sent to the regulators, which facilitates operator s everyday tasks. After theoretical analysis, the practical analysis of the potential improvements with UMS software was conducted: All real costs were considered: costs that a utility has to bear during the software implementation, taking into account the real market prices All potential benefits were evaluated: benefits that were possible to simulate using the real advanced UMS software on DEMO network, with real data Determined costs and benefits were compared for the purposes of cost/ benefits analysis Cost analysis Fig. 1: Geographical network view. All real costs that the utility has to bear during the UMS software implementation were analysed: Software licences (licences of the UMS system itself as well as the necessary third 92 65th AMEU Convention 2016

2 party licences (e.g. operating system for the workstations)) Hardware (servers, workstations, communication devices, video wall, etc.) Implementation services (system design, network data migration, system development, system installation, testing, training of staff) Different typical taxes Typical project duration (from the contract signing to the system commissioning, Go-Live) is from one to two years. On one hand, complexity of the system can vary: from the simple SCADA system to the complex SCADA/DMS/OMS system. On the other hand, the size of the utility s network can vary: from around electrical customers (meters) to several millions customers. These two factors directly reflect on the project duration and price. Therefore, different cases were analysed regarding software complexity and electrical customer number. Determined results were ranked considering the software complexity and presented per electrical customer: in this way utilities of different sizes can easily evaluate their potential costs: 3-10/customer. From this rank, average value of 7/customer can be taken into account. Furthermore, the average lifecycle of this system ten years was taken into account. The estimated investment is valid in the moment of project signing, however, all additional costs during ten years of the project lifecycle should be considered as well: System maintenance Periodic system upgrade Also, average depreciation rate or loan annual payment was considered. All initial and additional costs were recalculated for the initial year of the project and presented per electrical consumer. Total average costs are: 2/consumer/year. Benefit analysis Using the advanced UMS software on the DEMO network with real data, in the simulation mode, all potential benefits that particular functions can achieve were analysed [1]: Reduction of technical power losses: using optimisation functions Reduction of the expenses for the normal system operation: using outage management applications Voltage profiles improvement: using Volt/ Var optimisation Investments postponement: using planning tools Benefits were first analysed with software simulations [11]. The results were then compared with the experiences of the real distribution utilities that have implemented UMS software. Screenshot with the geographical network view of the UMS software which was used for the simulations is presented in the continuance (see Fig. 1). Basic network data is presented in Table 1. The screenshot of the main network data, taken in the UMS software is presented in continuance [11] (see Fig. 2). Technical losses reduction Utility is forced to pay for energy lost in transmission and distribution from their own budget therefore the motive for the loss reduction is clear. Using optimisation functions the average loss reduction rate was analysed: Network reconfiguration: determines the optimal topology of the network (optimal radial configuration) regarding power losses. Output of the function is the list of switching steps that will transfer the network from its current state to the optimal one Volt/Var optimisation [5,6]: determines Fig. 2: Basic network data. Parameter Value Customer number Number of transformer stations HV/MV Number of transformer stations MV/LV Number of MV heads (10 or 20 kv) Energy injected in the network Energy losses Energy consumption Price of purchased electrical energy Value of the annual injected electrical energy (AIEE) Price for using the network GWh 149 GWh 1094 GWh 30 /MWh 37,303 M 20 /MWh Annual utility revenue (PRA) 21,880 M Peak power Table 1: Basic network data. 226 MW the optimal positions of the tap changers and capacitor banks (voltage and reactive power flow management) regarding power losses Potential benefits were first evaluated individually, per function and then in the coordinated mode. As expected, the best results are achieved in the coordinated mode. 65th AMEU Convention

3 Reduction of normal operation expenses Reducing the outage time directly means more delivered energy and lower penalties for the non-delivered energy [1,4]. The achieved results are presented in the continuance [11] (see Figs. 3 and 4). UMS presents the potential power losses in percent of the total losses. Considering the typical correction factor for the powerenergy ration, this result is re-calculated and presented in the percent of the energy losses and then compared with the total energy transferred through the system, over one year. Fig. 3: Network reconfiguration report. Fig. 4: Volt/Var optimisation report. Benefit type Method Savings of the total annual transferred energy (%) Energy losses reduction Network reconfiguration, Volt/Var optimisation Normal operation expenses reduction Voltage profiles improvement Investment postponement Annual savings per meter (Eur/consumer/ year) 0,5 1 OMS, FLISR 1 2 Volt Var optimisation 0,5 1 Optimisation functions Planning tools 1 2 Total benefits 3% (Eur/consumer/year) Table 2: Total benefits. The final result is presented as the percent of the total annual energy transferred through the system that can be saved: 0,5%. Expected results are confirmed with the experience of the distribution utility in Italy, supplying over 30-million electrical customers. This utility has managed to reduce the technical losses by 4% annually, or, 0,5% of the total transferred energy [3]. Typical outage duration was considered, when the outage is resolved without software support and when the outage is resolved with support of the outage management applications: FLISR (Fault Location, Element Isolation, Supply Restoration): management of the large medium voltage outages, with support of the field equipment OMS (Outage Management System): outage management in the nonobservable network parts, based on the customer trouble calls Also, by smart fault location, isolation and supply restoration, UMS is reducing the number of necessary manoeuvres in the field and directly prolonging the lifecycle of the equipment. Simulated results were compared with the outage rate over the year, in order to determine the total annual energy savings. Results have shown savings of the total annual transferred energy by 1%. Expected results were compared with the experience of the utility in Texas (US), with half a million consumers. This utility has managed to save energy by 1% by using FLISR and OMS applications daily. Voltage profile improvement In this case, Volt/Var optimisation was considered again, however, now with a different optimisation criteria, combining [7]: Peak demand reduction Voltage deviation reduction (voltage profile improvement) Energy efficiency improvement Furthermore, distribution generators can be used as resources, promoting their utilisation, production and participation in the market. Average demand peak was analysed for different seasons in order to determine the results on the overall year basis. Average savings of the total annual transferred energy are 0,5%. Expected results were confirmed with the experience of the utility in North Carolina (US), supplying 1,5-million consumers. This utility has managed to reduce the peak demand by 300 MW yearly (around 3% of the total peak), and by that save around 0,5% of the total annual transferred energy. Investment postponement Investments in the network development and 94 65th AMEU Convention 2016

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5 Economic profitability factors new equipment are the highest investments for the utility. Therefore, reduction or postponement of these investments mean significant savings [10]. On one hand, by operation optimisation (as described in previous chapters), existing devices are used more efficiently. On the other hand, UMS planning tools enable smart planning of the network reinforcement. UMS planning tools first make the long term load forecast (several years in advance, accounting also for the non-technical variables such as the growth of the population), and then suggest different scenarios for the optimal connections of the new equipment. Also, optimal connections of the new automation equipment can be determined, accounting for the potential improvement of the system performance indices. In order to determine the results accurately, one real medium sized utility's experience with UMS planning tools was considered. Results have shown average savings of the total annual transferred energy by 1%. Expected results were confirmed with the Short explanation experience of the distribution utility in Serbia, with around customers. This utility has managed to make savings of around 1% of the total annual transferred energy by using UMS smart planning tools. Total benefits Total benefits described in the previous chapters are presented in Table 2. Cost/benefit analysis Value Profitability factor Describes the profitability of the project. 3 times Payback time Return on investment (ROI) Internal rate of return (IRR) Presents the time in which initial investment will return, and the project becomes profitable. Describes the added value (benefit) which the project will bring in the lifetime, over invested amount. Shows how attractive the investment is comparing with the average rate of the capital. Table 3: Economic profitability factors. 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This simplifies the selection task, making the KP100L Solar Inverter the right choice every time. 3,3 years 2 times 57,8% Finally, the evaluated costs and benefits were compared for the purposes of the cost/benefits analysis [1,9]: Costs: 2/consumer/year Benefits: 6/consumer/year Profitability: three times In order to increase the sensitivity of the analysis and the results precision, total costs and benefits, over the project lifecycle were analysed considering the average depreciation rate: Total costs: 14,4/meter/10 years Total benefits: 44/meter/10years Profitability: three times Pick up the phone or for a quote! +27 (0) info.sa@eu.omron.com Also, based on the evaluated costs and benefits, standard economic factors of the profitability were evaluated (see Table 3). Conclusion The aim of this paper was to present the cost/benefit analysis of the UMS system implementation in utilities of different sizes. First, the implementation costs were analysed, using the real market prices. Then, potential benefits that could be achieved were analysed, first with simulations using the real UMS software, and then the achieved results were compared with the experiences of the real utilities that have implemented UMS software. Finally, the costs and benefits were compared for the purposes of the cost/benefit analysis. Observations made: UMS projects show high profitability rate: returned money is three times bigger than the investments UMS project have short period of the investment return: the entire investment is returned in the first third of the project lifecycle UMS project show high income: income after returned investments are two times bigger than the investment. References [1] N Katic: Benefits of Smart Grid Solutions in Open Electricity Market, Acta Polytechnica Hungarica, Vol.10, No.2, 2013, pg [2] ME Baran and FF Wu: Network Reconfiguration in Distribution Systems for loss reduction and load balancing, IEEE Trans. On Power Delivery, Vol.4, No. 2, April [3] G di Lembo, et. al.: Reduction of Power Losses and CO 2 emission: Accurate network data to obtain good performances of DMS systems, Proceedings CIRED 20th Conference Prague, Prague, Czech Republic, [4] M Ristic: Serbian Utility Taps into Savings using DMS, Transmission & Distribution World, May [5] I Roytelman, et. al.: Volt/Var Control for Modern Distribution Management System, IEEE Trans. on Power System, Vol. 10, No. 3, August 1995, pg [6] V Strezoski, et. al.: Voltage Control Integrated in Distribution Management Systems, Electric Power Systems Research, No. 60, 2001, pg [7] B Simpson: Implementation of a SCADA/DMS on a Square Mile Grid to Reduce Peak Load and Optimize Network Performance, DistribuTECH, SAD, [8] Elektrovojvodina ( rs/), Chosen data regarding consumption, Report 2014, Novi Sad, Serbia, drustvo/energetski-pokazatelji [9] N Katic, at. al.: Power distribution automation profitability and sensitivity analysis, SEEEI conference, Israel, [10] M Ćalović and A Sarić: Planiranje razvoja elektroenergetskih sistema u regulisanom i deregulisanom okruženju, Technical faculty in Cacak, [11] UMS software for the distribution network management, Schneider Electric DMS NS V2.75.2, Contact Juliana Katic, Schneider Electric, juliana.katic@schneider-eletric-dms.com 96 65th AMEU Convention 2016