INTEGRATED DEMAND-SIDE MANAGEMENT COST-EFFECTIVENESS AND OPTIMIZATION METHODOLOGY

Transcription

1 INTEGRATED DEMAND-SIDE MANAGEMENT COST-EFFECTIVENESS AND OPTIMIZATION METHODOLOGY Eric Woychik 1, Mark S. Martinez 2, and Ken Skinner 3 OVERVIEW Across the U.S., demand-side management (DSM) 4 activities, budgets and programs continue to grow in importance and to increase in overall magnitude in response to state and national policy. The effectiveness of these programs can benefit greatly from better integration and optimization in planning and operations. In the smart grid context, both the optimal integration and the full valuation of DSM costeffectiveness (CE) have been challenging and difficult to achieve. 5 Important interactive effects between measures and full optimization have been difficult to quantify, which in the long run cause poor investment decisions, misalignments, lost opportunities, and stranded benefits. This paper contends that much greater planning and operational efficiency (and resulting benefits) can be captured through robust valuation and optimization of the grid and the full integrated DSM (IDSM) portfolio, which will in turn show greater business case opportunities. Water related benefits, including embedded-energy-in-water, can also be further integrated and optimized. A critical outcome of better integration and optimization is to effectively and systematically optimize consumer engagement, with corresponding cost reductions. 1 Strategy Integration, LLC. 2 Southern California Edison Company. 3 Integral Analytics, Inc. 4 Demand Side Management and Integrated Demand-Side Management (IDSM) are here defined to include energy efficiency (EE), demand response (DR), distributed generation (DG), and customer-side storage (ST). 5 E. Woychik and M. S. Martinez, Integrated Demand Side Management Cost-Effectiveness: Is Valuation the Major Barrier to New Smart-Grid Opportunities? ACEEE Summer Study, August 2012, Asilomar, CA. 1

2 Overall, IDSM transaction costs the contractual costs of doing the entire deal-- can be substantially reduced as well. With SmartConnect TM (smart grid), the use of optimization techniques is increasingly feasible and desirable at both the planning and operational stages. Optimization enables greater benefits, more effective consumer engagement and implementation, and increased efficiency in operations, which are just some of the advantages of the IDSM approach to integration and optimization. SCE and other California utilities are in a unique position to more effectively orchestrate large DSM portfolios with access to customer data, tools for consumer engagement, distribution and transmission system expertise, and expertise in wholesale markets. In this paper we demonstrate how to 1) chart a course to perform this integration and optimization, 2) achieve greater benefits and lower costs through IDSM, and 3) optimize the planning and use of DSM with the suite of smart meter rates and services. In the context of SCE s IDSM pilot projects, we explain how these projects may be best used to test and guide the development of a more comprehensive approach to IDSM cost-effectiveness through better integration and optimization. The paper first presents previous IDSM findings and recommendations as further context. Second, the Edison SmartConnect TM smart meter infrastructure and rate programs and consumer engagement are outlined. Third, a methodology to enable optimization engines is explained. And fourth, expected outcomes are summarized. The aim is to develop a comprehensive IDSM CE and optimization framework that fully captures the full benefits and tradeoffs in the SmartConnect TM context. GUIDEPOSTS -- PREVIOUS IDSM COST-EFFECTIVENESS FINDINGS AND RECOMMENDATIONS A set of useful guideposts seem useful that derive from specific key findings in the June 2011 IDSM Cost- Effectiveness White paper, as follows 6 : The IDSM customer focused approach to present all DSM options/measures at once in a coordinated strategy -- is vastly different and aims to make greater use of customer data and regional trends. Methods to capture and use automated metering infrastructure and Smart Grid data can enhance IDSM cost-effectiveness by providing better information on which related measurements, assumptions, and inputs are based. The use of customer-specific distribution and local market data will increase the accuracy of IDSM cost-effectiveness calculations. 6 Woychik E., et al., 2011, Integrated Demand-Side Management Cost-Effectiveness White Paper, for the California Integrated Demand-Side Management Task Force, May 6. 2

3 Specific utility distribution circuit data and planning information can be used to better define deferrable costs with IDSM resources. Inaccuracies that stem from the averaging of DSM data may result in the incorrect selection of IDSM resources. Erroneous conclusions about IDSM cost-effectiveness result because of inaccurate and inconsistent calculation methods and assumptions, lack of updated assumptions, and separate uncoordinated CPUC proceedings. The use of statistics and probability distributions can help define critical inputs, including IDSM value and long-term economic and hedging benefits, which then better define cost-effectiveness results. A three step IDSM cost-effectiveness framework can be utilized in the short term and be continually developed to capture greater accuracy in the long term. Consistent with the above findings, the IDSM White Paper presents these recommendations 7 : Initiate efforts to identify guidelines for a consistent IDSM cost-effectiveness framework that then provide greater accuracy and consistency in methods and assumptions. The focus of IDSM cost-effectiveness should be on development of a common, comprehensive methodology based on the integration of the SPM with additional valuation methods and local and regional data. Development is needed of 1) plans and methods to validate estimates of customer load with customer data, 2) a system to define IDSM resource fit, qualifications, and the full set of benefits, and 3) a cost-effectiveness calculator that uses advanced methods and local, regional, and market data. Specific distribution circuit data and transmission data should be used to enable the estimation of otherwise less certain deferrable costs with IDSM resources. A three-step IDSM cost-effectiveness methodology is recommended as follows: 1. Identify the full set of IDSM measures and estimate the deferred energy and capacity savings of each combination of measures. 2. Calculate the potential to reduce, and increase, energy costs, distribution circuit costs, capital budget costs, transmission needs, and market opportunities that are available through the California Independent System Operator (CAISO). 3. Estimate cost-effectiveness with properly defined benefits and costs for each SPM test, using a set of methods that extend beyond avoided cost calculations. 7 Ibid. 3

4 An important conclusion is that new processes and methods are needed within the context of the current Standard Practice Manual (SPM) framework to provide an optimal approach for IDSM cost-effectiveness. THE Edison SMARTCONNECT TM CONTEXT 8 The Edison SmartConnect TM project encompasses advanced automated metering infrastructure (AMI), smart pricing, and a set of consumer engagement services. This forms a significant part of the context for IDSM implementation, particularly the integration of this combined set of resources with the smart grid. The recent annual SCE report of energy and demand savings associated with the Edison SmartConnect TM programs focuses on the following consumer service feature Peak-Time Rebate Critical Peak Pricing Time-Of-Use Pricing Summer Discount Plan Consumer Web Presentment o Bill-to-Date estimation o Bill Forecast estimation Budget Assistant (to forecast and alert customers about expected monthly electricity bill impacts) In order to assess optimization, a number of features are employed to analyze each SmartConnect TM program type and fulfill reporting requirements. Overall program evaluation, overlap with other DSM programs, and measurement gaps are being examined. Additional analysis is being developed about specific DSM programs and customer behavior, including DSM program interrelationships as well as customer participation to forecast future DSM related activities. These considerations can be fully incorporated into the IDSM CE methodology. INTEGRATED DSM COST-EFFECTIVENESS AND OPTIMIZATION The following four elements are proposed to develop a framework for the integrated cost effectiveness of IDSM. The framework is robust in that integrated benefits are measured across both demographic and spatial segments. Cost effectiveness is measured using actual hourly data explicitly accounting for the weather covariance that is prevalent between prices and loads during extreme weather conditions. Further, the framework measures the operational and technological capabilities that are being 8 4

5 implemented to fulfill the Edison SmartConnect TM deployment. The goal of the analysis is to determine the net present value of time-dependent utility avoided cost savings, capacity and energy impacts, and customer (residential, commercial, and industrial) bill savings that can be derived from customer preference scenarios, to optimize the planned deployment and operations with IDSM. Of specific interest is in the integrated CE of energy efficiency, controllable loads, distributed generation and renewable alternatives to traditional IOU supply. The focus is on the achievable economic potential of benefits. The algorithms use distributed centralized control to optimize load reductions with traditional supply to minimize the cost of service while maximizing utility and/or customer benefits. Four Steps to Integrated DSM Cost-Effectiveness Step 1: Target Marketing and Consumer Engagement -- In short, this allows SCE to spot high value participants by program. The customers who are the most willing and have the economic motivation to use packages of IDSM measures can be targeted, based on a continuum of acceptance for specific programs, in part based on demographics, history of program involvement and program characteristics. Different customer segments, for example the set of customers who have high bills and loads, can be identified from interval data, in geographic segment-based clusters. Expected customer preferences can also be defined based on existing data on customer participation in DSM, pricing, and billing programs. This then suggests gateway characteristics and likely customer acceptance of specific IDSM measures. Beyond mere consumer segmentation of so called hogs and dogs 9 for IDSM measures, this approach makes explicit use of consumer engagement history, provided in part by the Smart-Connect (AMI data) interface, and a host of demographic and gateway decision points. Step 2: Locational T&D Benefits -- This step defines customer class-specific distribution and transmission (T&D) system impacts at the feeder level, with and without IDSM measure packages, guided from the analysis in Step 1. This is a special (geographic) and time-based assessment that measures the usage impacts and circuit overload at specific utility assets that are in place and planned. The existing inventory of DSM resources are layered onto the T&D assumptions previously generated from existing T&D power flow models. Specific customer load profiles, with existing and new IDSM measures can be fully evaluated, including the adoption of Plug-In Electric Vehicles (PEVs). This approach to layering customer loads on the T&D system provides the basis for discrete T&D benefits, fully accounting for existing infrastructure, established engineering assumptions, and the inventory of previous DSM measures, as 9 Here hogs refers to consumers of substantial energy and capacity, while dogs refers to consumers that use little energy and capacity. 5

6 well as the newly proposed IDSM measures. Customer value-of-service (CVOS) can also be used to further ratio the value of IDSM in particular load clusters, as it reflects the distribution costs to mitigate potential outages in the base-case (before IDSM) scenario. By including CVOS, these capabilities to reflect specific time-varying, local connected loads can be matched with customer preferences for reliability and directly integrated to fully reflect the value of IDSM in each situation. Step 3: Locational Covariance and Option Value -- The cost-effectiveness assessment can be significantly enhanced based on the inputs from Steps 1 and 2, and taken much further with the more comprehensive summation of benefits (and costs) that reflect the expected value or market value that can be anticipated. Expected or market value incorporates the uncertainty of demand, weather, demographics, various contingencies (e.g., forced outage), commodity (input) variations, and overall price fluctuations. This amounts to a probability (density) distribution around the expected or market values. Deterministic (point-source) estimates of avoided cost or market prices, however, cannot capture these uncertainties and variations. The option value is very similar to standard option value methods used to value supply-side resources. 10 Possibly more critical to the capture of expected or market value is the covariance of the data on loads, demand, weather, contingencies, and the like. The covariance of key factors effecting weather sensitive measures intensifies at extreme conditions. Expected value or market prices further reflect the extremes as they are multiplicative in their combined effect. The simple use of weather variation, beyond the normal weather-year assumption, illustrates the need to fully capture the probability of variation in the basic avoided and market price assumptions. Beyond this set of probability-based variations, many IDSM measures can be used in alternative roles especially if they are dispatchable in an electric market. This means that a direct load control program or peak-time-rebate can be bid into the market as a resource and dispatched by the CAISO as needed in the sub-market where it is most valuable. The concept of option value captures both the probabilistic elements in key market variables and the true option value of IDSM resources that can be use opportunistically in a set of submarkets. It is this option value that power plants claim and obtain financing to enter the market. It only seems appropriate that IDSM programs should be similarly valued based on critical probabilities and market opportunities. Step 4: Distributed Optimization to Maximize Benefits: Optimization of the use of a set of IDSM measures in a building or facility and in aggregate across numerous buildings is increasingly possible with distributed technologies and software packages that take account of the customer conditions and needs, weather, and market prices, cost of service and much more, all in near real time. Focusing jointly on 10 See, A. Eydeland and K. Wolyniec, Energy and Power Risk Management, (Wiley) 2003, at pp ; V. Kaminski Ed., Managing Energy Price Risk, (Risk) 1999, at pp

7 customer level energy management and system level cost of service, this optimization of IDSM measures is a post-planning benefit that can be incorporated into the cost-effectiveness assessment provided in Step 3. Optimization of a single building or a portfolio of controllable end-uses is a process of meeting customer needs and preferences subject to energy budget considerations. Opportunities to pre-cool, remotely turn on and off end-uses, float through a temperature zone (but stay within limits), and maximize the economic value of energy services in the market place are all possible with advanced optimization and control. As it is difficult to anticipate the uses and conditions in a building, the development of benefits from this requires work to establish a set of key assumptions. Conservatively defined, however, optimization of customer conditions, system level cost of service and market opportunities will produce significant benefits that can be included in the cost-effectiveness of IDSM measure packages. Optimization within the context of an IDSM framework is further described in the following section. The Optimization Engines Historically, the use of optimization techniques has been difficult in light of the complexity of the separate tasks that are involved in conceiving, planning, executing, and operating DSM programs and measures. With major advances in both data availability (largely from smart grid innovations) and analytic techniques (largely software and programing) optimization of DSM at the planning and the operational stages is feasible and highly productive. Optimal IDSM program design, targeting (by customer and grid location), and valuation can be more effective and efficient in normal utility operations as a result of advanced AMI technology, more granular data, and advanced software applications. Optimization engines have been developed to increase both customer and utility value through better decisions with management of customer loads and distributed renewable generation in coordination with grid level balancing and contingency operations. The major benefits are to reduce energy delivery costs and customer bills, and improve reliability while avoiding costly infrastructure development projects. These benefits are now more achievable and transparent through advances in distributed optimization engines. Depending on customer preference and satisfaction constraints, the optimization engine can assume partial management of any combination of end-use energy consuming or producing (e.g. demandresponse, distributed energy resources such as storage, and renewables) devices, including peak-time rebates and other pricing programs, appliances such as central air conditioning, electric heating system, electric clothes dryer, electric water heater, and Plug-In Electric Vehicles. Focused research has helped to build the utility business case to demonstrate the financial benefits by optimizing specific load and supply resources that are likely to occur in a future-state electric grid. The 7

8 evaluation proceeds in phases, residential end uses, commercial and industrial premises level benefits and aggregate benefits across many controllable facilities or distributed resources. The three phases of the optimization analysis are further described below. In PHASE 1 we determine the business case based on optimization (e.g. including load scheduling) for example to evaluate the opportunities for residential customers, in part by calculating the optimal dispatch strategy for specific residential appliances. The results determine utility energy and capacity savings as well as customer bill savings for various end use appliance combinations. In PHASE 2 we can expand the analysis to cover commercial and industrial customers, including the set of expected dynamic pricing programs. The analysis will additionally provide commercial and industrial customer bill savings by load type (e.g. motor, pump, lighting) and correlate this with the total utility capacity and energy costs avoided. Several strategic and tactical management questions can be answered with Phase 2 results, including: A quantification of how much utility avoided costs, capacity, and energy may be impacted by distributed optimization of commercial and industrial customer loads; A quantification of customer bill savings that are achievable from distributed optimization of commercial and industrial customer loads. An understanding of the competitive threat or opportunity from third party smart grid optimization and load aggregation; Determining stranded cost and lost revenue risk. In PHASE 3 we can move the analysis beyond the end user by exploring the benefits of aggregated, decentralized control of load. The analysis specifically considers the benefits of load as a resource, working interactively with traditional supply, transmission and distribution assets. System level benefits are quantified, including: Supply Improve power plant efficiency, Provide frequency regulation, ancillary services and load following, Provide non-spinning reserve, Firm renewables Transmission Avoided congestion fees 8

9 Distribution Provide voltage and frequency support, Reduce reactive power (VAR), improve power factor, Improve power quality, Mitigate outages, Defer system upgrades (load leveling), Integrate localized intermittent renewable and distributed resources, End user Improve customer satisfaction (reduce bills and increased reliability), Mitigate Value of Lost Load, (VOLL) Reduce customer energy bills The optimization demonstrates the rate-payer value of load leveling, peak shaving, load-shifting and load reduction at the substation, transformer, feeder and wholesale grid levels. The system evaluates load optimization as a dispatchable resource available for energy supply, ancillary services and CAISO dispatch. The benefits are tested in the context of the distributed metering, two-way communication and control envisioned in the following chart. Optimization engines (OE), shown by the red circles, provide IDSM benefits throughout the system through both active control and passive intelligence. 9

10 At the planning stage, the best possible generator, substation and circuit data that are available are used in the modeling process. The data from these systems include bench test algorithms whereby the modeling process will drive/generate actual system results. Model algorithms are used to pre-process optimizations below the substation level returning only the information required to assure global optimum for the substations. Each modeled substation contains approximately three to four circuits. Each circuit contains a finite number of residential, commercial and industrial facilities, transformers and distribution feeders. The analysis measures the sensitivity to demand-side assets, control assumptions, and the diversity of controllable resources. NOTIONAL RESULTS FOR INTEGRATION AND OPTIMIZATION The program results for primary features of the integration and optimization can be defined separately in terms of benefits and costs, compared to baseline deterministic results (from point-source assumptions) that represent average IDSM program design and average customer uptake. Figure 1 below presents notional results for IDSM programs that proceed from a current baseline CE analysis to then de-average customer and utility program benefits and costs with use of the following four methods: Target Marketing and Consumer Engagement (Engagement) of specific consumers through targeting Locational T&D Benefits (Location) of consumer loads in light of transmission and distribution costs and constraints Locational Covariance and Option Value (Covariance /Option) to determine both how much specific variables impact each other and to determine the value of resources in their optional uses Distributed Optimization to Maximize Benefits (Optimization) of the portfolio of IDSM resources both at the planning stage and operational stage Compared to the average deterministic Baseline results, these steps, in sequence, lead to a much more comprehensive and thus greater aggregation of benefits in CE terms, as Figure 1 suggests. Figure 1: IDSM Portfolio Benefits and Costs 10

11 The first layer, to achieve more effective program integration and optimization, is consumer engagement (Engagement), directed by a full analysis of the customer base to target those customers that are the best candidates for specific IDSM measures. In colloquial terms as explained above this is to identify the hogs and dogs in IDSM resource terms for any specific customer group and area based on billing data, load data, demographics, building size and type, weather zone, and local economic variables. The second layer can be to look at the program specific distribution and transmission (T&D) related loadflow analysis, based on local utility engineering input, which overlays the DSM inventory (existing) as well as the planned DSM (proposed) to be valued. This nets the (Locational) results for T&D capital cost deferral and energy cost reduction (losses) at each customer premise down to the customer circuit level. These results can be combined with the Engagement (screen) results to show the direct locational benefits and costs of customer location and T&D impacts. Engagement, as well as marketing, education, and outreach (ME&O), can be more focused and more effective have bigger bang for the buck when this more sophisticated two-pronged customer targeting is applied. These two steps also result in significant de-averaging of DSM impacts and the resulting CE This should also allow EM&V more effective and less expensive. 11

12 A third layer is to take the program results for Engagement and Location in order to perform more targeted Covariance/Option Value analysis. This step captures the covariance between weather, customer loads, T&D loads/costs, wholesale electricity costs, other economic variables, and provides insurance or option value. Arguably option value analysis is most applicable to weather dependent measures i.e. dispatchable resources, but the covariance aspect in this step is powerful and can be used for energy efficiency and distributed generation that are not dispatchable. The portfolio optionality or option value of these and related non-dispatchable resources can also be assessed in conjunction with the covariance benefits. Results can be presented as confidence intervals around weather normal expectations. Further, the potential for capital deferral of distribution upgrades can be identified (planning), as well as the expected reliability and power quality improvements (operational). This combined T&D and load modeling can be used to identify the impacts of specific assets and additional distribution level benefits. This modeling spatially identifies assets at risk, both with and without the proposed load management systems. Results are displayed in map layers, forecast charts, and tables to assist in planning. The fourth layer is to provide the optimization results, which requires a feedback loop. The benefits of optimal DSM portfolio operations must be pulled back into the benefits side of the assessment in Figure 1. The full planning optimization benefits (and costs) can then be shown in the sequence of programs that are provided along the ascending curve in Figure 1. This iteration is the nature of portfolio construction. Both the scope and scale of specific IDSM programs can be optimized and compared in terms of next increments of aggregate benefits and costs. At the same time the B/C ratio can be used to define the ultimate size and composition of the portfolio. As is fully integrated, probabilistically weighted B/C ratio approaches 1.0, this should represent comprehensiveness in program implementation and operations. EXPECTED OUTCOMES In this paper we provide a framework for CE analysis of IDSM portfolios. Although additional research is required to obtain a total value of the four step approach, existing studies are available and indicate the magnitude of benefits. Several studies commissioned by utilities are used to construct this methodology, some of which are currently ongoing. In these studies we demonstrated how customer engagement, locational T&D mapping, covariance, optionality, and optimization can be used to help determine the value and optimal amount or scale of weather dependent DSM within a portfolio. The choice between alternative energy and traditional energy sources is often determined by comparing the cost and benefits of each. Using appropriate decision criteria and IDSM integration/optimization methods the optimal mix of 12

13 resources can be determined to provide the best overall value for society. Our results provided an estimate of the integrated value of IDSM programs around planned capacity needs with a focus on weather related load and locational price/cost volatility. A recent study at one Western utility examined the added value of covariance and option value in the cost effectiveness calculations. The study used a region specific forward price hub, actual load shapes and temperature histories, and Monte Carlo simulation of prices at a 30.7% price volatility observed over 30 years of weather data, to determine that the added IDSM option value of a residential and small commercial air conditioning cycling program. Over a 10 year life, the program value is approximately 17% greater than what the baseline results would indicate. Excluding the locational covariance and option IDSM value would result in the use of much less demand response than is optimal. For the residential and small commercial program we find the baseline TRC ratio equal to Including the added IDSM value, the corrected TRC benefit/cost ratio is equal to While the expected total TRC ratio is 2.90, the historic regression on prices, weather and load prove that the 95% confidence interval will permit TRC values between 3.28 and In short, the additional IDSM value using step 3 locational covariance and option value of the integrated framework is significantly greater (17%) than the baseline results. We expect steps 1, 2 and 4 will provide similar significant results. CONCLUSION This paper proposed new valuation methods and extends the current research on IDSM costeffectiveness. Specifically, we propose a four step framework for IDSM CE valuation. Compared to the average deterministic Baseline results, these steps, in sequence, lead to a much greater aggregation of benefits in CE terms. Target Marketing and Consumer Engagement to direct specific DSM measures to specific consumers Locational T&D Benefits of consumer loads in light of transmission and distribution costs and constraints Locational Covariance and Option Value to determine both how much specific variables impact each other and to determine the value of resources in their optional uses Distributed Optimization to Maximize Benefits of the portfolio of IDSM resources both at the planning stage and operational stage 13

14 The framework is robust in that integrated benefits are measured across both demographic and spatial segments. Cost effectiveness is measured using actual hourly data explicitly accounting for the weather covariance that is prevalent between prices and loads during extreme weather conditions. Further, the framework measures the operational and technological capabilities that are being implemented to fulfill the Edison SmartConnect TM deployment. Initial results indicate that more IDSM energy and capacity are needed to fulfill term planning requirements and can be developed more economically than traditional generation and grid storage options. Based on this approach we find that the current decision methods favored by utility commissions significantly undervalued alternative energy choices by ignoring the locational value, covariance and insurance value, and the risk adjusted return for IDSM. 14