Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.     

Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers

To the extent that demand response represents an intentional electricity usage adjustment to price changes or incentive payments, consumers who exhibit more-variable load patterns on normal days may be capable of altering their loads more significantly in response to dynamic pricing plans. This study investigates the variation in the pre-enrollment load patterns of Korean commercial and industrial electricity customers and their impact on event-day loads during a critical peak pricing experiment in the winter of 2013. Contrary to conventional approaches to profiling electricity loads, this study proposes a new clustering technique based on variability indices that collectively represent the potential demand–response resource that these customers would supply. Our analysis reveals that variability in pre-enrollment load patterns does indeed have great predictive power for estimating their impact on demand–response loads. Customers in relatively low-variability clusters provided limited or no response, whereas customers in relatively high-variability clusters consistently presented large load impacts, accounting for most of the program-level peak reductions. This study suggests that dynamic pricing programs themselves may not offer adequate motivation for meaningful adjustments in load patterns, particularly for customers in low-variability clusters.     

Examination of optimum benefits of customer and LSE by incentive and dynamic price-based demand response

ABSTRACT

The balancing approach of electricity generation and consumption is an essential part of a reliable power system. The mismatch between supply and demand may also cause fluctuation in the power system. This study proposes an incentive and penalty-based demand response (I&P-DR) program for improving the profitability of both LSE and customers. First, we use a parameter that weighs the financial benefits of LSE and customers and provides considerable economic benefits to both sides. Second, an incentive and penalty (I&P) price scheme have been employed to recompense and penalize customers and reduce the electricity demand at peak hours. Finally, the study analyzes the importance of (I&P-DR) and its impact on customers’ sensitivity during peak intervals. Simulation results showed that the flexibility to consumption can be brought through the application of the proposed (I&P-DR) program and also provide the financial benefits to both, customers and LSEs.     

Optimal regulation of renewable energy: A comparison of Feed-in Tariffs and Tradable Green Certificates in the Spanish electricity system

Incentives for renewable energy based on Feed-in-Tariffs have succeeded in achieving high levels of renewable installed capacity. However, these incentives have not been responsive to market conditions or price signals, imposing in some cases a great financial burden on consumers when Renewable Energy Sources reached significant levels. A way out of this problem could be a market mechanism where incentives respond to the level of investment on renewables. We explore this issue comparing a regulatory system based on Tradable Green Certificates, able to react to market changes, to a Feed-in-Tariffs incentive scheme. We model the strategic interaction between participants in the electricity pool and the Tradable Green Certificates market and focus on the optimal regulation for the retailer segment, which generates the desired demand for green certificates as a decreasing function of the certificate price. We then calibrate our theoretical model with data from the Spanish electricity system for the period 2008–2013. Simulations show that a green certificate scheme could both achieve the 2020 targets for renewable electricity and reduce regulatory costs. However, the role of regulators is still important, since setting the right target for renewable electricity affects the cost burden of the system.     

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

This paper proposes a novel structure to optimize the operational strategies of responsive farms for day‐ahead peak shaving. To achieve the aim, the modern irrigation system of farms, including groundwater, surface water, and booster water pumps, are modeled mathematically. To develop the demand response (DR) potentials of the farms, electrical storage systems, and self‐generation facilities, including thermal distributed generations and on‐farm solar sites, are addressed. In order to facilitate the integration of the agricultural DR programs into the electricity market, a mathematical formulation for agricultural demand response aggregator (ADRA) is suggested. The ADRA participates in the day‐ahead electricity market on behalf of the responsive farms. To overcome the price uncertainty of the electricity market, a robust optimization approach is addressed. This approach determines the robust decisions of farms in the worst‐case realizations of the uncertain electricity price. Regarding on‐farm solar sites located in rural areas, a data‐driven approach is used to estimate the solar power generation of a significant number of sites without needing to install costly communication and measurement infrastructures. Finally, the proposed approach is implemented on agricultural lands in the northeast of Iran. The numerical results show that the suggested approach provides day‐ahead peak shaving for the power systems meeting the crop's water requirements.     

The price elasticity of electricity demand in South Australia

In this paper, the price elasticity of electricity demand, representing the sensitivity of customer demand to the price of electricity, has been estimated for South Australia. We first undertake a review of the scholarly literature regarding electricity price elasticity for different regions and systems. Then we perform an empirical evaluation of the historic South Australian price elasticity, focussing on the relationship between price and demand quantiles at each half-hour of the day.     

Fostering economic demand response in the Midwest ISO

The Midwest Independent System Operator (MISO) runs the power grid in 14 states and one Canadian province and has a peak demand of some 116,000 MW. Its operational area is richly supplied with reliability-triggered demand response programs such as direct load control of residential appliances and curtailable/interruptible rates for commercial and industrial customers. However, economic demand response programs are lacking. This paper discusses three different ways in which such programs can be introduced in the wholesale energy markets run by MISO. These include, first, an approach in which utilities and load serving entities move retail customers to dynamic pricing and other time-based pricing rates; second, an approach in which these same entities and possibly third-parties bid price responsive demand curves into the wholesale market; and third, an approach in which demand response is bid as a supply resource into the wholesale market.     

考虑用户需求响应的电网过载风险评估与控制

智能电网的发展为用户参与电力市场运行提供了坚实的基础,根据用户需求特性与电价之间的杠杆作用,建立了用户需求弹性矩阵,结合用户需求、市场电价与系统负荷之间的关系,提出考虑用户需求响应的过载风险评估和控制方法,有效地降低电网过载的风险,并采用IEEE39节点系统仿真计算和分析验证了该方法的有效性。     

Electricity demand for South Korean residential sector

This study estimates the electricity demand function for the residential sector of South Korea with the aim of examining the effects of improved energy efficiency, structural factors and household lifestyles on electricity consumption. In the study, time series data for the period from 1973 to 2007 is used in a structural time series model to estimate the long-term price and income elasticities and annual growth of underlying energy demand trend (UEDT) at the end of the estimation period. The result shows a long-term income elasticity of 1.33 and a long-term price elasticity of −0.27% with −0.93% as the percentage growth of UEDT at the end of the estimation period. This result suggests that, in order to encourage energy efficiency in the residential sector, the government should complement the market based pricing policies with non-market policies such as minimum energy efficiency standards and public enlightenment.     

An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.     

Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

In this paper, a weighted combination of different demand vs. price functions referred to as Composite Demand Function (CDF) is introduced in order to represent the demand model of consuming sectors which comprise different clusters of customers with divergent load profiles and energy use habitudes. Derived from the mathematical representations of demand, dynamic price elasticities are proposed to demonstrate the customers’ demand sensitivity with respect to the hourly price. Based on the proposed CDF and dynamic elasticities, a comprehensive demand response (CDR) model is developed in this paper for the purpose of representing customer response to time-based and incentive-based demand response (DR) programs. The above model helps a Retail Energy Provider (REP) agent in an agent-based retail environment to offer day-ahead real time prices to its customers. The most beneficial real time prices are determined through an economically optimized manner represented by REP agent’s learning capability based on the principles of Q-learning method incorporating different aspects of the problem such as price caps and customer response to real time pricing as a time-based demand response program represented by the CDR model. Numerical studies are conducted based on New England day-ahead market’s data to investigate the performance of the proposed model.     

The price elasticity of the residential demand for electricity in the Vepco service area

In this study, we derive seasonal estimates of price elasticities of the residential demand of electricity in the Vepco service area from a detailed integrated economietic and end-use model that does not constrain the demand function to constant elasticities with respect to the explanatory variables. The elasticity coefficients derived from the model conform to expectations based on theoretical considerations.We also examine the structural stability of the demand function for residential electricity in the Vepco service area during 1969: 2–1980: 4. Two shifts are detected, one at the beginning of 1976 and the other at the end of 1978. The first shift is attributed to the delayed reaction of Vepco residential customers to the oil embargo and the subsequent increases in energy prices; the second relates to increased energy consciousness brought about by continued high prices of energy and also by encouragement from the federal government to conserve.     

Robust bidding and offering strategies of electricity retailer under multi-tariff pricing

In this paper, an electricity retailer seeks to determine selling price for end-user consumers under fixed pricing (FP), time-of-use pricing (TOU) and real-time pricing (RTP). Furthermore, in order to provide power exchange between the retailer and the power market, bidding and offering curves should be prepared to bid and offer to the day-ahead market. Therefore, this paper proposes a robust optimization approach (ROA) to obtain optimal bidding and offering strategies for the retailer. To achieve this, ROA is used for uncertainty modeling of power market prices in which the minimum and maximum limits of prices are considered for uncertainty modeling. Lower and upper bounds of price is consecutively subdivided into sequentially nested subintervals which allows formulating robust mixed-integer linear programming (RMIP) problem. The proposed RMIP model helps retailer to select a robust decision in the presence of market price uncertainty. Furthermore, the bidding and offering curves of the retailer are obtained from sufficient data through solving these problems. Meanwhile, the uncertainty of customers demand and variable climate condition are modeled based on stochastic programming. To validate the proposed robust optimization model, three case studies are evaluated and the results are compared.     

Short- and long-run time-of-use price elasticities in Swiss residential electricity demand

This paper presents an empirical analysis on the residential demand for electricity by time-of-day. This analysis has been performed using aggregate data at the city level for 22 Swiss cities for the period 2000−2006. For this purpose, we estimated two log–log demand equations for peak and off-peak electricity consumption using static and dynamic partial adjustment approaches. These demand functions were estimated using several econometric approaches for panel data, for example LSDV and RE for static models, and LSDV and corrected LSDV estimators for dynamic models. The attempt of this empirical analysis has been to highlight some of the characteristics of the Swiss residential electricity demand. The estimated short-run own price elasticities are lower than 1, whereas in the long-run these values are higher than 1. The estimated short-run and long-run cross-price elasticities are positive. This result shows that peak and off-peak electricity are substitutes. In this context, time differentiated prices should provide an economic incentive to customers so that they can modify consumption patterns by reducing peak demand and shifting electricity consumption from peak to off-peak periods.     

The large-scale integration of wind generation: Impacts on price,reliability and dispatchable conventional suppliers

This work examines the effects of large-scale integration of wind powered electricity generation in a deregulated energy-only market on loads (in terms of electricity prices and supply reliability) and dispatchable conventional power suppliers. Hourly models of wind generation time series, load and resultant residual demand are created. From these a non-chronological residual demand duration curve is developed that is combined with a probabilistic model of dispatchable conventional generator availability, a model of an energy-only market with a price cap, and a model of generator costs and dispatch behavior. A number of simulations are performed to evaluate the effect on electricity prices, overall reliability of supply, the ability of a dominant supplier acting strategically to profitably withhold supplies, and the fixed cost recovery of dispatchable conventional power suppliers at different levels of wind generation penetration. Medium and long term responses of the market and/or regulator in the long term are discussed.     

Electricity demand in Kazakhstan

Properties of electricity demand in transition economies have not been sufficiently well researched mostly due to data limitations. However, information on the properties of electricity demand is necessary for policy makers to evaluate effects of price changes on different consumers and obtain demand forecasts for capacity planning. This study estimates Kazakhstan's aggregate demand for electricity as well as electricity demand in the industrial, service, and residential sectors using regional data. Firstly, our results show that price elasticity of demand in all sectors is low. This fact suggests that there is considerable room for price increases necessary to finance generation and distribution system upgrading. Secondly, we find that income elasticity of demand in the aggregate and all sectoral models is less than unity. Of the three sectors, electricity demand in the residential sector has the lowest income elasticity. This result indicates that policy initiatives to secure affordability of electricity consumption to lower income residential consumers may be required. Finally, our forecast shows that electricity demand may grow at either 3% or 5% per year depending on rates of economic growth and government policy regarding price increases and promotion of efficiency. We find that planned supply increases would be sufficient to cover growing demand only if real electricity prices start to increase toward long-run cost-recovery levels and policy measures are implemented to maintain the current high growth of electricity efficiency.     

Advanced mechanisms for the promotion of renewable energy—Models for the future evolution of the German Renewable Energy Act

The German Renewable Energy Act (EEG) has been very successful in promoting the deployment of renewable electricity technologies in Germany. The increasing share of EEG power in the generation portfolio, increasing amounts of fluctuating power generation, and the growing European integration of power markets governed by competition calls for a re-design of the EEG. In particular, a more efficient system integration and commercial integration of the EEG power is needed to, e.g. better matching feed-in to demand and avoiding stress on electricity grids. This article describes three different options to improve the EEG by providing appropriate incentives and more flexibility to the promotion mechanism and the quantitative compensation scheme without jeopardising the fast deployment of renewable energy technologies. In the “Retailer Model”, it becomes the responsibility of the end-use retailers to adapt the EEG power to the actual demand of their respective customers. The “Market Mediator Model” establishes an independent market mediator responsible to market the renewable electricity. This model is the primary choice when new market entrants are regarded as crucial for the better integration of renewable energy and enhanced competition. The “Optional Bonus Model” relies more on functioning markets since power plant operators can alternatively choose to market the generated electricity themselves with a premium on top of the market price instead of a fixed price.     

The theory of maximum kW demand charges for electricity

The kilowatt (kW) demand charge characterizes most electricity price schedules for industrial customers, yet it has not been analysed thoroughly (compared with time-of-use rates). This article shows how such charges complicate interpretations of past estimates of the industrial demand for electricity. Using a utility maximization (cost minimization) model, we derive consistent demand curves, which are shown potentially to be subject to discontinuities.     

Efficient pricing and investment in electricity markets with intermittent resources

Facing growing technological and environmental challenges, the electricity industry needs effective pricing mechanism to promote efficient risk management and investment decisions. In a restructured electricity market with competitive wholesale prices and traditionally regulated retail rates, however, there are technical and institutional barriers that prevent dynamic pricing with price responsive demand. In regions with limited energy storage capacity, intermittent renewable resources present special challenges. This could adversely affect the effectiveness of public policies causing inefficient investments in energy technologies. In this paper, we present an updated economic model of pricing and investment in restructured electricity market and use the model in a simulation study for an initial assessment of renewable energy strategy and alternative pricing mechanisms. A key objective of the study is to shed light on the policy issues so that effective decisions can be made to improve efficiency.     

How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models

Based on a dynamic model for the high/low range of electricity prices, this article analyses the effects of Germany's green energy policy on the volatility of the electricity market. Using European Energy Exchange data from 2000 to 2015, we find rather high volatility in the years 2000–2009 but also that the weekly price range has significantly declined in the period following the year 2009. This period is characterised by active regulation under the Energy Industry Law (EnWG), the EU Emissions Trading Directive (ETD) and the Renewable Energy Law (EEG). In contrast to the preceding period, price jumps are smaller and less frequent (especially for day-time hours), implying that current policy measures are effective in promoting renewable energies while simultaneously upholding electricity market stability. This is because the regulations strive towards a more and more flexible and market-oriented structure which allows better integration of renewable energies and supports an efficient alignment of renewable electricity supply with demand.