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This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers. [ABSTRACT FROM AUTHOR]

As a flexible electricity pricing mechanism, critical peak pricing (CPP) is one of the important means of demand response under the electricity market. The existing CPP research does not take into account the carbon emission problem of units and weakens the difference between the use of terminal loads on critical peak days and non-critical peak days in the establishment of electricity price model, so this paper studies the feedback mechanism of CPP on coal consumption of power generation side units and proposes a dynamic CPP mechanism that takes into account terminal consumption satisfaction and coal consumption of power generation side units. Firstly, the influence mechanism of CPP on the power generation side is studied. Secondly, the consumer psychology theory is used to construct a user demand response model under critical peak days and non-critical peak days. Then, based on the difference in load usage of end users on critical peak days and non-critical peak days, a multi-objective CPP optimization model that considers the benefits of coal consumption and end user electricity expenditure on the power generation side is constructed. Finally, three scenarios were established to analyze the sensitivity of the user demand response model parameters, terminal satisfaction constraints, prices, and rate restrictions on CPP pricing, load improvement, and unit coal consumption reduction and verify the model's effectiveness. The results show that the proposed CPP optimization model has a significant effect on load improvement and carbon emission reduction; the user response gradient, terminal satisfaction, prices, and rate restrictions have a greater influence on the model optimization results, while the threshold and saturation values have little influence on the model optimization results. [ABSTRACT FROM AUTHOR]

Demand response (DR) is the short-term change of consumers energy usage patterns in response to power utility needs. The utility benefits from DR adoption in terms of better load management, reduction of peak power deficits, energy deficits, enhanced reliability, and improved economic performance. Consumers gain from energy bill savings and incentive payments. Accurately estimating the consumer baseline load (CBL) is critical for evaluating the success of a DR program implementation. Estimation of CBL helps to decide the volume of load curtailment, decide the incentives to be offered, and assess the impacts of consumer participation. The real-time domestic 11 kV feeder data of the 6.3 MVA substation of Goa electricity board, Goa, India is used to analyze the performance of the DR program. For CBL analysis time series average, adjustment, exponential moving average, decomposition, and forecasting techniques are applied. For performance evaluation of DR programs, the Artificial neural network (ANN) based CBL estimation technique is utilized. The ANN-based technique of estimation has better prediction accuracy, lower bias, and reduced variability as compared to other CBL estimation techniques. Consumer response to demand changes is computed using elasticity and consumer benefit function. The ANN-based CBL estimation and demand variation obtained from elasticity values are further used to analyze the technical and financial performance factors, for dynamic price and incentive-based DR program implementation. The research can assist a state power utility that is implementing smart grids in categorizing consumers, establishing a baseline, identifying potential demand reductions, and evaluating the feasibility DR programs. [ABSTRACT FROM AUTHOR]

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.

This study examines how electric utilities and regulators can encourage residential consumers to conserve electricity during the hottest summer days and shift electricity load from the day to off-peak, nighttime hours. We analyze a two-year field experiment involving 280 Texas households that explores approaches to conservation and load-shifting to enable emission reductions and reduce generation costs. Our critical peak pricing intervention reduces electricity consumption by 14% on the peak hours of the hottest days, leading to greenhouse gas emission reductions of about 16%. A key contribution of this study is the use of high-frequency appliance-level data. We show that 74% of the critical peak response is from reducing air conditioning. In a complementary nighttime pilot program, consumers respond strongly to lower prices by programming the timing of electric vehicle charging. Our work highlights how automation can influence the consumer tradeoffs relating to effort costs, discomfort, monetary incentives, and warm glow. This paper was accepted by Rajesh Chandy, Special Section of Management Science on Business and Climate Change. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2020.02074. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

The existing research on critical peak price (CPP) decision-making ignores the difference in risk appetite between industries within the consumer population, resulting in a serious lag in the enthusiasm of some users to respond to CPP, and unsatisfactory improvement of power systems and carbon emission reduction on the supply and demand side. Firstly, the problem of consumer risk appetite was comprehensively analyzed, and the industrial consumer population was secondarily stratified according to the influencing factors and the enthusiasm of responding to CPP, namely: stubborn, active and conformist, and quantitatively verified by cluster analysis. Secondly, by combing the relevant paths of CPP decision-making, the critical-peak window determination model and CPP multi-objective optimization model were constructed, and the calculation of relevant indicators was introduced. Finally, taking 10 industrial enterprises in a city in Sichuan Province as an example, the clustering method was used to verify the stratification results, and the index analysis method was used to measure the load and carbon emission improvement of two typical enterprises after CPP optimization. The results showed that the stubborn users insist on using electricity, the improvement effect of load and carbon emission reduction was poor, and additional production costs will be caused. The conformist and active users had high sensitivity to electricity price fluctuations, good load and carbon emission reduction improvement effects, and significantly reduced electricity costs. [ABSTRACT FROM AUTHOR]

Prior research has shown load reduction estimates from residential event-driven demand response programs (e.g., Critical Peak Pricing)using X of the highest Y days with a weather adjustment method are the best performing within the class of currently used baseline methods. However, they are still biased relative to estimates produced from randomized control trials (RCTs), the unbiased “gold standard” evaluation method. In this paper we identify underlying factors that cause some of the bias found in one commonly used baseline method. Rather than simply quantifying bias, our research provides a deeper understanding of what causes the bias and thus can be used to develop more accurate methods that are not subject to these underlying factors. Previous studies have compared various baseline methods relative to each other; however, because all baseline methods are biased, it is impossible to determine the true bias that exists in them. We have access to a unique RCT dataset: the Sacramento Municipal Utility District's study of critical peak pricing. Our analysis of 23 event days over two summers allows us to identify the true bias on load reduction estimates by using the RCT estimates as the unbiased gold standard against which we compare the estimates from the baseline methods. We found that spillover of energy reductions, from hours targeted by a program onto other hours, is one underlying factor that is a major cause of bias in baseline methods. We discuss alternative baseline methods that may not be subject to this same bias.

Prior research has shown load reduction estimates from residential event-driven demand response programs (e.g., Critical Peak Pricing)using X of the highest Y days with a weather adjustment method are the best performing within the class of currently used baseline methods. However, they are still biased relative to estimates produced from randomized control trials (RCTs), the unbiased “gold standard” evaluation method. In this paper we identify underlying factors that cause some of the bias found in one commonly used baseline method. Rather than simply quantifying bias, our research provides a deeper understanding of what causes the bias and thus can be used to develop more accurate methods that are not subject to these underlying factors. Previous studies have compared various baseline methods relative to each other; however, because all baseline methods are biased, it is impossible to determine the true bias that exists in them. We have access to a unique RCT dataset: the Sacramento Municipal Utility District's study of critical peak pricing. Our analysis of 23 event days over two summers allows us to identify the true bias on load reduction estimates by using the RCT estimates as the unbiased gold standard against which we compare the estimates from the baseline methods. We found that spillover of energy reductions, from hours targeted by a program onto other hours, is one underlying factor that is a major cause of bias in baseline methods. We discuss alternative baseline methods that may not be subject to this same bias.

Recent broad-based deployment of Advanced Metering Infrastructure (AMI) enables the opportunity for broader adoption of time-based rates, and the benefits that result have been sizable contributors to making the investments cost effective. However, some stakeholders have raised concerns about the assumptions underlying the benefits assessments in AMI business cases. Such concerns are especially acute for certain subpopulations of residential customers. Low income, elderly and chronically ill (i.e., vulnerable) customers are believed to have less load that can be shifted or reduced to capture bill savings, lack the know-how or wherewithal with which to curtail usage, likely have more limited financial resources which may compel them to avoid high priced periods by reducing electricity for essential usage potentially causing them physical harm, and more generally may be more adversely affected by higher bills, which might possibly result from certain forms of time-based rates. There is very limited existing literature that addresses these questions specifically with regard to vulnerable subpopulations. This paper, based on a larger report, extends the existing empirical literature on the experiences of low-income customers exposed to critical peak pricing, and provides the first glimpses into the experiences of the elderly and those who reported being chronically ill.

Recent broad-based deployment of Advanced Metering Infrastructure (AMI) enables the opportunity for broader adoption of time-based rates, and the benefits that result have been sizable contributors to making the investments cost effective. However, some stakeholders have raised concerns about the assumptions underlying the benefits assessments in AMI business cases. Such concerns are especially acute for certain subpopulations of residential customers. Low income, elderly and chronically ill (i.e., vulnerable) customers are believed to have less load that can be shifted or reduced to capture bill savings, lack the know-how or wherewithal with which to curtail usage, likely have more limited financial resources which may compel them to avoid high priced periods by reducing electricity for essential usage potentially causing them physical harm, and more generally may be more adversely affected by higher bills, which might possibly result from certain forms of time-based rates. There is very limited existing literature that addresses these questions specifically with regard to vulnerable subpopulations. This paper, based on a larger report, extends the existing empirical literature on the experiences of low-income customers exposed to critical peak pricing, and provides the first glimpses into the experiences of the elderly and those who reported being chronically ill.

This paper presents a day-ahead optimal EV demand management framework to reduce feeder peak demand while ensuring the benefits from energy arbitrage. Leveraging the demand flexibility of EV charging, when appropriately coordinated, holds promise for mitigating feeder demand stress. Existing literature lacks a comprehensive framework to facilitate active orchestration of EV demand flexibility for a hybrid scheme of peak load reduction and energy arbitrage due to limitations of existing pricing schemes to effectively harness EV demand adaptability. To this end, this paper proposes a three-stage EV scheduling strategy that optimally coordinates distribution feeder demand while efficiently allocating the candidate EVs. EV demand, estimated in first stage by an EV aggregator, is further optimized by distribution system operator (DSO) in the second stage leveraging charging flexibility of EVs employing a novel pseudo price function termed "critical peak included locational marginal prices" (CP-LMP). Finally, the third stage entails the EV aggregator maximizing EV allocation. Furthermore, a robust optimization-based operational planning framework is proposed to hedge the uncertainties in EV parameters and their distributions. Comprehensive analyses are presented to validate the efficacy of the proposed methodology. Depending on factors such as EV penetration, permissible load shifting, and charging station capacity, our approach demonstrates a notable 4%–10% reduction in peak load, underscoring the potential benefits of the proposed strategy in enhancing distribution system resilience and efficiency for rapid EV proliferation. • Day-ahead EV scheduling strategy considering peak load reduction and Energy Arbitrage. • A new pseudo-price function is proposed for the optimization. • A Robust optimization based operational planning strategy is proposed. • Uncertainties in arrival time and requested SoC are modeled. [ABSTRACT FROM AUTHOR]

Summary: Ever since its inception, the concept and application of demand‐side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load‐responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price‐based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price‐based demand response program. Furthermore, the stochastic‐based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence‐based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno‐economic performance indices were evaluated for all case studies, and a priority‐wise ranking is assigned based on the multi‐criteria assessment technique. [ABSTRACT FROM AUTHOR]

Abstract Demand Response (DR) provides both operational and financial benefits to a variety of stakeholders in the power system. For example, in the deregulated market operated by the Electric Reliability Council of Texas (ERCOT), load serving entities (LSEs) usually purchase electricity from the wholesale market (either in day-ahead or real-time market) and sign fixed retail price contracts with their end-consumers. Therefore, incentivizing end-consumers’ load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market. As the first-of-its-kind implementation of Coupon Incentive-based Demand Response (CIDR), the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements, individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets, as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts. A number of methodologies are developed for this special type of DR program including price/baseline prediction, individualized target setting and a lottery mechanism. This paper summarizes the methodologies, design, critical findings, as well as the potential generalization of such an experiment. Comparison of the EnergyCoupon with a conventional Time-of-Use (TOU) price-based DR program is also conducted. Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries, the effective cost for demand response providers in EnergyCoupon can be substantially reduced, while achieving a similar level of demand reduction as conventional DR programs.

Demand Response (DR) provides both operational and financial benefits to a variety of stakeholders in the power system. For example, in the deregulated market operated by the Electric Reliability Council of Texas (ERCOT), load serving entities (LSEs) usually purchase electricity from the wholesale market (either in day-ahead or real-time market) and sign fixed retail price contracts with their end-consumers. Therefore, incentivizing end-consumers' load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market. As the first-of-its-kind implementation of Coupon Incentive-based Demand Response (CIDR), the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements, individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets, as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts. A number of methodologies are developed for this special type of DR program including price/baseline prediction, individualized target setting and a lottery mechanism. This paper summarizes the methodologies, design, critical findings, as well as the potential generalization of such an experiment. Comparison of the EnergyCoupon with a conventional Time-of-Use (TOU) price-based DR program is also conducted. Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries, the effective cost for demand response providers in EnergyCoupon can be substantially reduced, while achieving a similar level of demand reduction as conventional DR programs. [ABSTRACT FROM AUTHOR]

Critical peak pricing (CPP) is a demand response program that can be used to maximize profits for a load serving entity in a deregulated market environment. Like other such programs, however, CPP is not free from the payback phenomenon: a rise in consumption after a critical event. This payback has a negative effect on profits and thus must be appropriately considered when designing a CPP scheme. However, few studies have examined CPP scheme design considering payback. This study thus characterizes payback using three parameters (duration, amount, and pattern) and examines payback effects on the optimal schedule of critical events and on the optimal peak rate for two specific payback patterns. This analysis is verified through numerical simulations. The results demonstrate the need to properly consider payback parameters when designing a profit-maximizing CPP scheme.

This article evaluates demand-side interventions aimed at reducing residential consumption during the peak energy periods. The interventions were applied to a sample of high-income households and included a set of text message reminders advising participants to reduce electricity use during peak hours. One group of participants received accompanying intra-day increases in peak-hour kWh rates, while another group of participants did not receive any price incentives. We find that intra-day price increases, though small in absolute magnitude, produced significant reductions in peak energy use. Reductions in use, as compared to a control group, were significantly higher among the pricing group compared to the group only receiving text messages, suggesting that pricing played a central role in influencing behaviour. Our results contribute to ongoing policy discussion about the effect of dynamic pricing on consumer energy demand. [ABSTRACT FROM AUTHOR]

Abstract Smart grid enables the two-way communication between the suppliers and consumers. Price-driven demand response (PDDR) is one of the important demand response categories that uses price of the energy as control signals to affect consumers’ electricity consumption. The current PDDR programs include critical peak pricing (CPP), time-of-use (TOU) pricing, and real-time pricing. In this paper, we provide a review of the PDDR studies. Detailed evaluations on advantages and disadvantages of each PDDR are provided. Concerns and future research challenges on PDDR are also addressed. It is believed that with the installation of smart meter infrastructures at residential households, price signal can be an efficient market tool for peak demand shaving, risk and reliability management, carbon emission reduction, and energy cost reduction. Highlights • A review on price-based residential demand response is proposed. • Evaluated critical peak pricing, time-of-use pricing, and real-time pricing programs. • A P2P trading method is proposed in the PDDR program. • Advantages and disadvantages of each price-based demand response are provided. • Price signal can be an efficient tool for residential demand response. [ABSTRACT FROM AUTHOR]

Dynamic pricing is an effective way to induce the customers to change their electricity usage patterns, critical peak pricing (CPP) event shows a great potential to reduce the peak demand and enhance the grid reliability. This paper investigates performances of the CPP experiment across aggregated group-level of electricity customers covering 16 commercial, 15 office and 50 residential objectives in the Kitakyushu Smart Community demonstration project. A data-based approach to the comparisons of electricity demand curves and distributions among time of use (TOU) and CPP events. Result confirms the differences in price-based responsiveness across types of customer, there is demand shaping effect during the CPP event period by adjusting the electricity price at 30-min interval. The power saving effects were obviously confirmed in residential and commercial sectors under CPP event program. A multiple linear regression model is used to examine the response effectiveness and weigh the influencing factors of the fixed time for the dynamic pricing scheme. For the no-residential part, the electricity consumption strongly depends on the variations in temperature, commercial consumer tends to respond more to the changes of electricity price than office sector. The residential customer shows a promising potential for load reduction corresponding the increasing electricity price. However, the effects depend on the preferences of residential customers to change their power usage pattern at real time of the CPP event period. [ABSTRACT FROM AUTHOR]

Along with the technical feasibility of microgrids, the importance of supervisory communications, computing, and control (CCC) systems that are necessary to operate a microgrid in a stable and economically feasible manner have been firmly established in the literature. While several investigators have proposed and demonstrated algorithms and applications of CCC, the functional entities and responsibility centers for realizing CCC within microgrids in the context of multiple independent customers and distribution system entities have not been situated and/or studied. In this context, the term emergent microgrid may be used to define an electrical network that consists of local generation, loads, and storage. Such a grid has the potential to be clustered as a microgrid, but does not have all the CCC features to become a full-fledged microgrid. In this paper, a simple electric utility platform (SEUP) that is an end-to-end solution with all the hardware/software interfaces and components necessary for augmenting emergent microgrids with CCC into full-fledged microgrids is presented. SEUP abstracts the details of sensing and CCC for the microgrid developers, installers, operators and end-users to provide a seamless system. SEUP is distinctive from other CCC platforms in that it is entirely based on publicly available infrastructure resources (wireless cellular networks, internet and cloud computing) without any intellectual property restrictions. The paper introduces the functional roles, main architecture, components and security features of SEUP along with the results from a sample application case of a laboratory-scale power system. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of demand response on households’ electricity consumption under the existence of the reference price effect. The reference price effect is observed in peak and shoulder hours. Consistent with reference price theory predictions, our results show that the reduction of electricity consumption is greater in the case where the current price is higher than the previous price than in the case where the current price is lower than the previous price. [ABSTRACT FROM AUTHOR]

This paper examines electricity critical peak pricing (CPP) as a measure for controlling electricity demand at critical peak times. This pricing scheme is designed to facilitate energy conservation not only inside but also outside the home. For this study, we surveyed consumer propensity to leave the home under CPP schemes and analyzed the impact of CPP on consumer cost. The results indicated that higher prices induce a higher rate of going out, while residential conditions such as population density and access to public transportation have a relatively small impact on leaving the home and average energy conservation. However, this is not always the case for aged households with limited mobility; residential conditions have a substantial effect on this segment of the population. Combined with a reduced ability to go out, electricity pricing has a greater negative impact on aged people. These results imply that improving accessibility through transportation development and urban compaction is an effective means of saving electricity alleviating the negative impact of CPP on the aged society of the future.

Current consensus holds that 1) passing through wholesale electricity clearing prices to end-use consumers will produce maximal efficiency gains and 2) simpler forms of time-varying retail rates will capture only a small portion of potential benefits. We show that neither holds in the presence of capacity costs typical in U.S. wholesale markets. Using an optimization model describing the short-term problem faced by an electricity retailer, we find hourly prices that optimally pass through capacity costs. We estimate benefits for a retailer using these prices as well as optimal configurations of a number of time-varying rate structures. Testing a range of realistic assumptions, we find that in the absence of a well-designed demand charge, passing through clearing prices may miss up to three quarters of the benefits possible from optimal hourly prices. By contrast, a simpler critical peak pricing structure enables retailers to achieve approximately two-thirds of the total possible benefits. [ABSTRACT FROM AUTHOR]

Critical peak pricing (CPP) is an electricity demand response technology that has great potential to lower the electricity cost and eliminate the need for more GHG emitting power plants. Many utilities start to offer CPP as the default electric service for industrial customers in their market design. When a manufacturing customer defaults to CPP, it is vital to understand what it is and how it will influence their energy budget and facility operations. In addition, given the option to opt-out to a time-of-use (TOU) rate, it is not always easy to tell whether the switch will result in higher bills or more GHG emissions. These questions will be answered in this paper. Specifically, we will model and compare both CPP and TOU rates to gain more accurate knowledge regarding annual electric costs and GHG emissions. With these results, manufacturing enterprises will be able to make more informed decisions on which service to choose and how to use electricity while fulfilling their role for sustainability by enrolling. The case study results show that for industrial customers with production flexibility, with proper rescheduling of electric use, they can save money by adopting CPP, while contributing to reducing GHG emissions. The savings on the annual electric bill can be 30.45% with a simultaneous GHG emissions reduction of 5.63% for an average industrial customer. [ABSTRACT FROM AUTHOR]

A summer residential CPP experiment in Japan that varied CPP prices by as much as 10 times the baseline rate induced 6.5–8.8% additional maximum electricity-saving behavior, but the behavior’s price sensitivity was limited. Of the total amount of electricity saved during CPP hours, 50.4–60.9% was attributed to maximum-saving behavior. [ABSTRACT FROM AUTHOR]

A deregulated market environment in power industries offers utilities or load serving entities the chance to make profit by pursuing a suitable operational strategy. However, the volatility of the real-time market clearing price raises a price risk issue because the load serving entity sells electricity to customers at a relatively frozen retail rate. One method to hedge price risk is to implement various dynamic pricing schemes in the retail sector in order to reflect the volatility of the real-time market clearing price to the retail rate. This paper presents several analyses for designing one such pricing scheme, namely critical peak pricing for a profit-maximizing load serving entity. Specifically, how the parameters of critical peak pricing affect profit based on the price responsiveness model of customers is analyzed. In this process, a method for solving the events scheduling problem is used as a tool for the analyses. Furthermore, we offer intuitive guidelines and rules for selecting those parameters that maximize the profit of the load serving entity. Finally, the suitability and practicality of the presented analyses are verified by numerical simulations with forecasted data on the real-time market clearing price and demand. [ABSTRACT FROM AUTHOR]

Dynamic pricing of electricity is receiving growing attention as a key enabler of the smart grid and a solution for promoting grid resilience to increasing penetration of renewable sources. Despite various dynamic pricing programs are contemplated and actively implemented by utilities in many developed economies, corporate decision makers are not aware of the extent to which their businesses can adapt to the programs and its associated risks and opportunities. This study investigates the demand response of commercial and industrial businesses to the Korean critical peak pricing pilot which was implemented in the winter of 2013. The demand responses of 802 businesses covering 34 commercial and industrial categories are evaluated and characterized by constructing their individual baseline loads. The results resoundingly confirm substantial differences in demand response across and within the segments, with the magnitude of response closely correlated with the firms' expenditure shares of electricity. It is thus suggested that conventional business classification is not sufficient enough to characterize firms' demand response to dynamic pricing plans, calling for firm-level, data-based approaches to consumer segmentation and electricity services. This study also identifies two important types of response behavior to which due attention has not been paid: shortterm learning effect — that is, the demand responsiveness increasing with CPP event experiences — and seemingly inefficient behavior of preparatory and recovering reductions in off-peak hours neighboring critical peak hours. [ABSTRACT FROM AUTHOR]

Economic model predictive control (EMPC) has recently gained popularity for managing energy consumption in buildings that are exposed to non-constant electricity prices, such as time-of-use prices or real-time prices. These electricity prices are employed directly in the objective function of the EMPC problem. This paper considers how electricity prices can be designed in order to achieve a specific objective, which in this case is minimizing peak electricity demand. A primal-dual formulation of the EMPC problem is presented that is used to determine optimal prices that minimize peak demand. The method is demonstrated on a simulated community of 900 residential homes to create a pricing structure that minimizes the peak demand of the community of homes. The pricing structure shows that homes should be given a 1-h peak demand duration, and that the peak prices given to the homes should be spread unevenly across 6 h of the afternoon. [ABSTRACT FROM AUTHOR]

In this paper, we provide demand impact estimates of a critical peak pricing (CPP) program tested in the summer of 2011. We develop econometric models that examine demand responses of participants in "opt-in," "opt-out," and "tech only" CPP programs. Opt-out customers received bill protection while tech only customers received in-home displays alerting them of critical peak times, but they were not placed on the CPP rate. Our results indicate that opt-in customers reduced critical peak period demand the most while opt-out customers' appear to attenuate their reduction because of bill protection. Additionally, we refine our findings using participant survey responses. In general, we find participants in test groups whose environmental or "green" attitude is high had the strongest demand response. [ABSTRACT FROM AUTHOR]

SUMMARY Electricity demand response is considered an effective approach to balance the electricity demand and supply with existing infrastructure of generation, transmission, and distribution. A majority of existing literature on the electricity demand response has mainly centered on the commercial and residential building sectors while the application for the industrial sector is largely neglected. This paper presents a methodology for the application of a typical demand response program, Critical Peak Pricing (CPP) program, for the manufacturing enterprises. The configuration of the reservation capacity (in kilowatt, kW) in the CPP program, which plays a critical role in the cost of the final bill charge, will be identified by optimal production scheduling for the typical manufacturing systems with multiple machines and buffers. Mixed Integer Nonlinear Programming formulation is used to establish the mathematical model with the objective to minimize the electricity bill cost as well as the potential penalty cost due to the non-fulfillment of the target production. An approximate technique is introduced to find a near optimal solution, and a numerical case study is used to illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines electricity critical peak pricing (CPP) as a measure for controlling electricity demand at critical peak times. This pricing scheme is designed to facilitate energy conservation not only inside but also outside the home. For this study, we surveyed consumer propensity to leave the home under CPP schemes and analyzed the impact of CPP on consumer cost. The results indicated that higher prices induce a higher rate of going out, while residential conditions such as population density and access to public transportation have a relatively small impact on leaving the home and average energy conservation. However, this is not always the case for aged households with limited mobility; residential conditions have a substantial effect on this segment of the population. Combined with a reduced ability to go out, electricity pricing has a greater negative impact on aged people. These results imply that improving accessibility through transportation development and urban compaction is an effective means of saving electricity alleviating the negative impact of CPP on the aged society of the future. [Copyright &y& Elsevier]

In this paper, Time of Use and Critical Peak Pricing policies are designed and their effects on optimized operation is evaluated. For this purpose, an optimal operation algorithm is employed and the relevant production costs are derived. As far as the Time of Use policy is concerned, such costs are observed to be different in two time intervals of the day, due to the loads changing effect and the production of renewable energy. Whereas in the Critical Peak Pricing Policy, a peak time interval is identified to set up different pricing rates. The optimization algorithm used to carry out the work described above minimizes the microgrid's energy losses, the production costs and the Green House Gas emissions and controls the power exchange with the main distribution grid. Loads control is indeed also another control variable that may help to get these objectives through active demand. [ABSTRACT FROM AUTHOR]

Under fixed retail rates, electricity demands have no incentive to change their usage pattern. In contrast, under time-varying rates, suppliers can reflect the actual supply cost during the peak hours in retail prices enabling end-users to respond to the prices and reduce their loads or shift their loads from the high-price hours to the low-price hours. Load shifting may increase the reliability of the system, but may increase CO_2 emission if coal-fired generators are the marginal generator used during the low- price hours. CO_2 emission taxation is one of ISO's options to deal with the CO_2 emission problem. In this paper, a new time-varying rate design method considering the CO_2 emission taxation is suggested. In addition, market power problem and non-shifting elastic loads are considered. The suggested method is applied to a test market with critical peak pricing (CPP) and real-time pricing (RTP) and the results are analyzed. Also the suggested method is applied to the Korean power system and the results are analyzed. [ABSTRACT FROM PUBLISHER]

Abstract: This paper analyzes data from 483 households that took part in a critical-peak pricing (CPP) experiment between July and September 2004. Using a regression-based approach to quantify hourly baseline electric loads that would have occurred absent CPP events, we show a statistically significant average participant response in each hour. Average peak response estimates are provided for each of twelve experimental strata, by climate zone and building type. Results show that larger users respond more in both absolute and percentage terms, and customers in the coolest climate zone respond most as a percentage of their baseline load. Finally, an analysis involving the two different levels of critical-peak prices – $0.50/kWh and $0.68/kWh – indicates that households did not respond more to the higher CPP rate. [Copyright &y& Elsevier]