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Texas's windfarm construction continues unabated, despite wind energy's cannibalization effect on wind generation's investment incentive and the rising popularity of short-term wind power purchase agreements (PPAs). Using ERCOT's monthly data for Jan-2011 to Dec-2021, we develop spot energy price forecasts by time of day (TOD) and their standard deviations to derive the efficient frontiers (EFs) for spot and forward energy sales of a risk-averse windfarm developer under a short-term wind PPA of not more than ten years. These EFs reveal a windfarm's operating revenue forecast tends to increase with the PPA's forward energy prices. Further, the windfarm's revenue risk and forecast move in tandem, akin to the risk-return relationship rooted in Markowitz's portfolio theory. Hence, the developer tends to sell one hundred percent (<100%) of the windfarm's energy output at forward energy prices that are above (below) spot energy price forecasts by TOD for recovering wind generation's levelized cost of energy. JEL Classification: Q41, Q42, Q47, Q48 [ABSTRACT FROM AUTHOR]

For railroad operators with a highly public nature, ensuring the safety of passengers and revitalizing the areas along the line are both tasks to be accomplished. To enhance passenger safety, it is necessary to keep congestion levels below a certain level on platforms and trains. Since traffic congestion generally occurs at certain times, such as in the morning, it is important to disperse traffic congestion during rush hours. In the past, railroad operators have encouraged passengers to voluntarily commute off-peak, but this has not worked as an incentive for passengers simply by improving comfort. Because of this failure, rail operators are considering a method of setting fare prices based on the time of day and congestion levels. However, past studies suggest that few users change their behavior because those who choose the time of day to use the railroads do not always coincide with the fare payers. Therefore, we devised a crowd management system in which coupons redeemable at stores along the rail line are given directly to the customers, encouraging voluntary off-peak commuting and stimulating the local economy. This system features dynamic updating of coupon ranks based on congestion forecast information from measured congestion levels to enhance the effectiveness of the service. The results of a three-month verification experiment that cooperated with Shizuoka Railway confirmed that 7.1% of passengers took congestion avoidance actions and that passengers who obtained coupons got off at a specific station an average of 29% more often. [ABSTRACT FROM AUTHOR]

Time‐of‐use (TOU) electricity prices are implemented based on the response characteristics of users. TOU prices guide the electricity consumption behaviour of users and make them actively respond to peak shaving and valley filling. In this paper, the conventional psychology response model of consumers is improved based on the history data of customer response to the TOU price. The paper defines four types of response load curves and proposes the response reliability indices. Additionally, it establishes the probability models of customer response. According to the characteristics of customer response, the sensitivity analysis of influencing factors and the risk analysis of customers are carried out according to the probability model of response. For instance, risk and characteristics analyses of the customer response were performed using the load data of residential areas in Nanjing city. The results indicate that the probability distribution of the customer response is approximate to the trapezoid distribution. The sequence of sensitivity coefficients of customer response factors is as follows: customer income, type of customer, and electrical equipment. The proposed method provides a scientific basis for electricity price implementers to accurately grasp the response behaviour of the user and establish a reasonable demand response strategy. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS). [ABSTRACT FROM AUTHOR]

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market's generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE's annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are −5.34 for solar capacity demand and −5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid's efficient operation and system reliability. However, adopting such proposals also slows the grid's pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid's stakeholders. [ABSTRACT FROM AUTHOR]

Based on the large-scale penetration of electric vehicles (EV) into the building cluster, a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed, for improving the safe and economical operation problems of distribution network. The system power loss and node voltage excursion can be effectively reduced, by taking measures of time-of-use (TOU) price mechanism bonded with the reactive compensation of energy storage devices. Firstly, the coordinate charging/discharging load model for EV has been established, to obtain a narrowed gap between load peak and valley. Next, a multi-objective optimization model of the distribution grid is also defined, and the active power loss and node voltage fluctuation are chosen to be the objective function. For improving the efficiency of optimization process, an advanced genetic algorithm associated with elite preservation policy is used. Finally, reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads. The proposed strategy is demonstrated on the IEEE 33-node test case, and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV; in the meantime, via reasonable planning of the compensation capacitor, the remarkably lower active power loss and voltage excursion can be realized, ensuring the safe and economical operation of the distribution system. [ABSTRACT FROM AUTHOR]

Many tariffs are used for electricity pricing. Electricity bills can be reduced by choosing the optimal working hours of appliances and for appropriate tariffs in smart homes. In this study, Linear Programming (LP) and Simulated Annealing (SA) methods are realized and compared each other to find minimum bill. The multi-time tariffs that have different energy unit prices at different times of the day are preferred. Energy Management System (EMS) shifts time slots of some appliances to cheap energy unit prices. Optimal load control is applied to prevent high peak demand because there may be overload in system because of shifting of working hours of appliances. Mathematical model of the problem is constructed and LP method is solved by GAMS and SA technique is realized by C# program. The cost table consisting of the energy unit prices of each time slots is used as input. Optimum electricity cost, working hours of the appliances and peak to average ratio are achieved by two different solutions and the results are compared. According to result, LP gives lower cost than SA. [ABSTRACT FROM AUTHOR]

With the increasing deployment of battery storage devices in residential electricity networks, it is essential that the charging and discharging of these devices be scheduled so as to avoid adverse impacts on the electricity distribution network. In this paper, we propose a non-cooperative, price-based hierarchical distributed optimisation approach that provably recovers the centralised, or cooperative, optimal performance from the point of view of the network operator. The distributed optimisation algorithm provides important insights into the appropriate design of contracts between an energy provider and their associated residential customers, who can themselves act as energy providers as well as consumers (e.g. due to rooftop solar photovoltaics and batteries) depending on the time of the day and on real-time prices. To make the presentation self-contained, and to highlight key properties of the price-based optimisation algorithm, the dual ascent method and its convergence properties are reviewed. The performance of the proposed price-based optimisation algorithm is validated on recent measurement taken from an Australian electricity distribution company, Ausgrid. In addition to analysing the results of the open loop solution, we investigate the effect of real-time prices in the closed loop using a model predictive control framework. [ABSTRACT FROM AUTHOR]

Highlights • Formulating a maximization model of expected social welfare by given a classification of smart home applications. • Representing operating process of household appliances by Markov decision process. • Dividing the optimization problem into two sub-problems in terms of users and energy supplier. • Solving the maximization model with distributed algorithms. • Simulation studies are given to show performance of the proposed algorithm. Abstract Demand side management (DSM) aims to match power demand to supply through cutting the peak and filling the valley. It is one of the most important factors in smart grid. The real-time pricing (RTP) scheme is an ideal method to adjust power balance between supply and demand. Considering the classification of smart home appliance (SHA) and the correlation of power consumption among users, a social welfare maximization model is proposed based on Markov decision process (MDP) in the research. A probabilistic transfer matrix has been introduced to characterize the elastic appliance in smart home. Several state transfer functions have been utilized to reflect the operating process of the semi-elastic appliance also. In this way, the specific characteristics of SHA can be fully embodied. In order to improve computing efficiency and protect the user's privacy, this project divides the optimization into two subproblems in terms of users and energy supplier (ES). At user side, a modified simulated annealing RTP algorithm is developed to solve the optimization problem. Also, at ES side, the dual sub-gradient method is used to solve the convex optimization problem in the paper. Finally, the simulation results validate the rationality and feasibility of the optimization model by the decentralized RTP algorithm. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) are incorporated into wind farms to gain more profits by shifting energy over time and to track predetermined power schedules. In operations, charging/discharging power of the BESS is adjusted flexibly to follow the power schedules of the wind-BESS hybrid systems (W-BESS-HS), which are set to be the sum of short-term predicted wind powers and charging/discharging schedules of the BESS. In order to extend lifetime of batteries, the BESS operation is subject to a sequential charging/discharging state sequence, which is predetermined according to time-of-use (ToU) pricing schemes. An iteration scheme is presented to update scheduled charging/discharging rates of the BESS according to simulation results based on sequential Monte-Carlo simulation (SMCS) technology so that the W-BESS-HS can not only meet a probabilistic requirement on generation schedule tracking but also gain further economic benefits by achieving a trade-off between punishments resulted from power deviations and wind power curtailment losses. In the SMCS simulation, a series of real-time indices are presented to evaluate performances of the W-BESS-HS at every dispatching interval and provide updating directions of the iteration scheme. The research work can provide theoretical support when operating the W-BESS-HS in ToU pricing schemes. [ABSTRACT FROM AUTHOR]

Abstract Time of use (TOU) tariffs, if widely adopted, could help make electricity more secure, clean and affordable. However, quite little is known about whether consumers will switch to a TOU tariff or what might increase uptake if switching rates are lower than required. This paper presents the results of a systematized review and meta-analysis combining the results of 66 measures of uptake to a variety of TOU tariffs across 27 studies conducted in six countries. It provides the first robust estimate of consumer demand and correlates of demand for TOU tariffs that is not based on the results from just a single study or tariff. Four main conclusions emerge. First, if consumers are left to opt-in to TOU tariffs, uptake could be as low as 1% unless efforts are made to close the intention-action gap, otherwise enrolment could reach 43%. Second, if enrolment is opt-out, uptake could approach 100%. Third, whilst national surveys indicate the potential appetite for TOU tariffs in a population, they are insufficient for predicting future TOU tariff adoption rates; the median proportion of domestic energy bill payers who say they would be willing to switch to a TOU tariff in national surveys is five times higher than the median enrolment rate to TOU tariffs offered by utilities. Fourth, real-time pricing tariffs, in which the price of electricity varies freely throughout the day, are less popular than static TOU tariffs which have fixed peak and off-peak rates. This paper discusses the limitations of opt-out enrolment for TOU tariffs and presents results suggesting that small upfront payments, bill protection and automation are promising alternative methods of increasing opt-in enrolment. Policymakers and researchers should now consider how recruitment will be performed, weighing up the benefits to society as a whole against the distributional impacts for individuals and groups. Highlights • Meta-analysis of international uptake to time of use tariffs across 27 studies. • If enrolment is opt-in, uptake is likely to fall between 1% and 43%. • If enrolment is opt-out, uptake could approach 100%. • Uptake measured in surveys is systematically higher than uptake to commercial time of use tariffs. • Real-time pricing tariffs are less popular than static time of use rates. [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]

Coincident peak pricing is used in several electricity markets to recover the embedded cost of infrastructure, such as transmission. In this approach, measured consumption at the time of the peak is used to set charges for that pricing period or a subsequent period. If transmission costs are truly sunk, then such a recovery is unlikely to be efficient. However, in the context of growing peak demand, new additions must be built. We consider the incentive properties of coincident peak pricing when related investments are not considered to be sunk, finding that it can reproduce the incentive properties of an ideal time-varying price. We also consider several variations on this assumption. [ABSTRACT FROM AUTHOR]

This study investigates a novel control algorithm for the home energy management system (HEMS) to monitor and schedule the electrical appliances to apply in any conventional houses. This is to reduce the electricity consumption and consequently electricity cost in places with time-of-use (TOU) pricing model. A hardware implementation is executed in a testbed house premise using in-house built control system, smart plugs as well as PV system as the supplementary source. In this system architecture, the overall consumption of the electrical appliances that run on the grid is limited by the algorithm and kept under the certain value that is fixed depending on the total consumption of the building and the capacity of the battery. If the predefined limit is crossed, the new appliances are shifted to run on battery instead of shifting to other time, and thereby human comfort is less violated. In the worst case scenario, where the electrical appliances are assumed to consume the maximum amount of electricity, a prototype implementation of the proposed algorithm achieves up to 15% of electricity cost reduction and ensures the minimum sacrifice in dweller's comfort. [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]

Due to the energy storage limitations of photovoltaic systems and the uneven time-of-use of electricity by users and the policies of different time-of-use electricity price, scheduling and dispatching of photovoltaic systems incurs irregular trend of electricity consumption and associated price hikes which influences by supply and demand. To this end, this paper proposes a novel model for photovoltaic system based on sensor networks system exploiting the historical factual data and the real-time data obtained from various sensors of the system, where adaptive particle swarm optimization and bacteria foraging algorithms are used for optimizing the scheduling process. The algorithm enhances the capacity of both global and local searches, and the convergence speed and estimation accuracy are promoted obviously compared to the other algorithm. Experiments results demonstrate that the ED model in this paper has the effect of peak cutting and valley filling, which ultimately bring the greatest economic benefits to the users. [ABSTRACT FROM AUTHOR]

The solution proposed here aims to optimize the electricity costs of a household, based on time-of-use rates, by reprogramming and automating appliances and devices that are major electricity consumers. It also offers real-time monitoring of the power usage, while providing predictive analyses and optimization solutions based on the collected data, to help the household occupants in making consumption-related decisions. A cloud platform is going to be used to connect home appliances, systems, sensors, and smart meter. The platform will combine the services offered by the Azure IoT Suite, chosen because it facilitates the rapid development of IoT solutions and provides several preconfigured solutions for quickly developing IoT systems. The solution is based on collecting disparate data from sensors and home appliances and processing and analyzing them, in order to assist the customer in identifying inefficiencies, setting specific sustainability goals, and balancing between the budget and the desired comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a robust midterm framework to determine optimal time-of-use (TOU) electricity pricing for retailers using intuitionistic fuzzy divergence based thresholding. It will analyze load profile and perform a comprehensive evaluation of load violation and uncertainty as well by exponential intuitionistic fuzzy entropy. The key advantage is that the proposed method will handle uncertain behavior of load and wholesale price with membership and non-membership functions. In the proposed algorithm client response to the retailer prices and the competition among rival retailers have been explicitly taken into account. The proposed scheme can be applied to several pricing policies where the peak-to-average ratio (PAR) will be decreased. The model is implemented on a realistic case in which the superior performance of the proposed method is demonstrated. [ABSTRACT FROM AUTHOR]

This paper studies the electric vehicle (EV) charging scheduling problem under a parking garage scenario, aiming to promote the total utility for the charging operator subject to the time-of-use (TOU) pricing. Different from most existing works, we develop a multicharging system incorporating the practical battery charging characteristic, and design an intelligent charging management mechanism to maximize the interests of both the customers and the charging operator. First, to ensure the quality of service for each client, we implement an admission control mechanism to guarantee all admitted EVs’ charging requirements being satisfied before their departure. Second, we formulate the charging scheduling process as a deadline constrained causal scheduling problem. Then, we propose an adaptive utility oriented scheduling (AUS) algorithm to optimize the total utility for the charging operator, which can robustly achieve low task declining probability and high profit. The charging operator can also apply the discussed reservation mechanism to mitigate the performance degradation caused by the charging information mismatching with vehicle stochastic arrivals. Finally, we conduct extensive simulations based on realistic EV charging parameters and TOU pricing. Simulation results exhibit the effectiveness of the proposed AUS algorithm in achieving desirable performance compared with other benchmark scheduling schemes. [ABSTRACT FROM PUBLISHER]

Through the application of flexible Time-of-Use (ToU) tariffs, demand side management (DSM) can be facilitated in order to alleviate grid congestion problems and potential network reinforcement. In this work, a novel approach to derive ToU tariffs for residential prosumers is described and the potential impact is verified in a pilot network within the distribution grid of Cyprus comprised of three hundred prosumers. This pilot network acts as a test-bed for defining the baseline scenario and subsequently verifying the developed ToU tariffs. The ToU block periods were determined by combining statistical analysis and a hybrid optimization function that utilizes annealing driven pattern search algorithms. The ToU rates were calculated by exploiting an optimization function that maintained a neutral electricity bill in the case where the load profile remained unchanged. The impact of the derived ToU tariffs was first analysed through a sensitivity analysis performed on the seasonal load profiles of the participants. The obtained sensitivity analysis results showed that for the summer and winter season, the maximum Load Factor (LF) was 42.83% and 33.33% respectively and occurred when load was shifted mainly to the off-peak period. Finally, with the ToU tariffs applied to the pilot network of prosumers, the effectiveness and potential response of the prosumers to the imposed tariffs, was verified. The results indicated that the LF was increased while the percentage of total consumption measured during peak hours was reduced by 3.19%, 1.03% and 1.40% for the summer, middle and winter season respectively. This led to the conclusion that the derived ToU tariffs are effective in persuading the prosumers to change their energy behaviour. [ABSTRACT FROM AUTHOR]

This article investigates a bi-objective scheduling problem on uniform parallel machines considering electricity cost under time-dependent or timeof- use electricity tariffs, where electricity price changes with the hours within a day. The aim is to minimize simultaneously the total electricity cost and the number of machines actually used. A bi-objective mixed-integer linear programming model is first formulated for the problem. An insertion algorithm is then proposed for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines. To obtain the whole Pareto front of the problem, an iterative search framework is developed based on the proposed insertion algorithm. Computational results on real-life and randomly generated instances demonstrate that the proposed approach is quite efficient and can find high-quality Pareto fronts for large-size problems with up to 5000 jobs. [ABSTRACT FROM AUTHOR]

In this paper, selling price determination problem for an electricity retailer has been studied. In the proposed model, the selling price is determined under fixed pricing (FP), time-of-use pricing (TOU) and real-time pricing (RTP). It is shown that the selling price determination based on RTP by the retailer can lead to the higher expected profit. Furthermore, to exchange power between retailer and power market, the bidding and offering curves should be prepared to bid and offer to the day-ahead market. Therefore, this paper proposes an information gap decision theory (IGDT) for obtaining of optimal bidding and offering strategies of retailer. IGDT assesses the robustness and opportunity decisions of optimal bidding and offering strategies in the presence of market price uncertainty while retailer considers whether robustness decision (risk-averse) or opportunity decision (risk-taking). It is shown that risk-aversion and risk-taker influence the expected profit and optimal bidding and offering curves. Meanwhile, the scenario-based stochastic framework is used for uncertainty modeling of market prices, client group demand and variable climate condition containing temperature, irradiation and wind speed. To validate the proposed model, three case studies are used and the results are compared. [ABSTRACT FROM AUTHOR]

A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup. [ABSTRACT FROM AUTHOR]

A Dual Command (DC) cycle "dynamic sequencing" method in unit-load multiple-rack AS/RS system under Time-Of-Use (TOU) electricity tariffs is applied in this paper. To make a type of energy-efficient model, overconsumption cost of on-peak period electricity, penalty cost of overconsumption of power, bounds on the total consumed energy, and accessible times of all facilities are considered in the model. Moreover, a Genetic Algorithm (GA) is developed to achieve a near-optimum solution to the suggested energy-based mathematical model with the objective of minimizing the total cost of the AS/RS system under TOU tariffs. Considering that no benchmark is obtainable in the literature, a Simulated Annealing (SA) algorithm is developed to certify the outcome gained. For supplementary confirmation, we compare the total cost of our model with that of single-tariff model and also conduct sensitivity analysis of the allowable amount of power consumption. The system throughput, in terms of time and cost, is calculated for the model too. In the last part, sixteen numerical examples with different numbers of required storage/retrieval orders are suggested to display the function of the proposed procedure. Our outcomes verified that GA is able to obtain favorable and closer optimal solutions and the TOU tariffs model get the minimum total cost. [ABSTRACT FROM AUTHOR]

Load participation is vital for the smart grid development. As an effective tool to improve reliability, stability, and financial efficiency of the power grids, demand response (DR) has brought significant financial and technical benefits to power systems. As one of the price-based DR programs with less control costs, the time-of-use (TOU) program has been applied as the default rate structure by many utility companies. To avoid financial risks and make the most profit from the market, utility companies treat TOU as an effective strategy to change customers’ electricity consumption patterns. As reported in many literature, existing TOU programs are not as effective as expected in many developed countries due to the complexity of human behaviors and disparities of residential customers. To examine whether to obtain different outcomes of TOU on the residential customers in developing countries, actual utility usage data from residential consumers in Shanghai, China, are analyzed in this paper. The result shows current TOU in Shanghai, China, has similar trends as TOU in developed countries. With high penetration level of renewable energy, an effective TOU program is urgently needed in the utility industry. In recent years, a creative TOU pricing structure has been introduced at the Electrical Reliability Council of Texas deregulated market, and it shows that the introduced “zero pricing” strategy has a significant impact on customers’ consumption patterns. The purpose of this research is to examine the key reasons that underlie ineffectiveness/effectiveness of TOU programs at residential level. [ABSTRACT FROM PUBLISHER]

As an essential characteristic of smart grid, demand response may reduce the power consumption of users and the operating expense of power suppliers. Real-time pricing is the key component of demand response that encourages power utilization in an efficient and economical way. In this paper, we study the real-time pricing scheme in smart grid with multiple retailers and multiple residential users using Stackelberg game. Additionally, the price competition among power retailers is formulated as a non-cooperative game, while the coordination among the residential users is formulated as an evolutionary game considering the private information of power retailers and residential users. The existence of Stackelberg equilibrium is proved. Moreover, two special algorithms are developed to solve the equilibrium. Numerical results show the convergence of algorithms, and also confirm the efficiency and effectiveness of proposed real-time pricing scheme. [ABSTRACT FROM AUTHOR]

Vehicle-grid integration (VGI) describes various approaches to link the electric power system and the transportation system in ways that may benefit both. VGI includes systems that treat plug-in electric vehicles (PEVs) as controllable load with a unidirectional flow of electricity, such as 'smart' or 'controlled' charging or time-of-use (TOU) pricing. VGI typically encompasses vehicle-to-grid (V2G), a more technically advanced vision with bidirectional flow of electricity between the vehicle and power grid, in effect treating the PEV as a storage device. Such VGI systems could help decarbonize transportation, support load balancing, integrate renewable energy into the grid, increase revenues for electricity companies, and create new revenue streams for automobile owners. This review introduces various aspects and visions of VGI based on a comprehensive review. In doing so, it identifies the possible benefits, opportunities, and barriers relating to V2G, according to technical, financial, socio-environmental, and behavioral components. After summarizing our sociotechnical approach and the various opportunities and barriers indicated by existing literature, we construct a proposed research agenda to provide insights into previously understudied and unstudied research objectives. We find that the majority of VGI studies to date focus on technical aspects of VGI, notably on the potential of V2G systems to facilitate load balancing or to minimize electricity costs, in some cases including environmental goals as constraints. Only a few studies directly investigate the role of consumer acceptance and driver behavior within such systems, and barely any studies address the need for institutional capacity and cross-sectoral policy coordination. These gaps create promising opportunities for future research. [ABSTRACT FROM AUTHOR]

The widespread proliferation of Electric Vehicles (EVs) can have a transformative effect on the electric power system. The power and energy consumed by EVs when charging is substantial, which has consequences on power system operation and planning. This paper identifies, evaluates, and proposes time-series seasonal autoregressive integrated moving average (ARIMA) models of aggregated EV charging station load. The modeling is based on 2 years of time-stamped aggregate power consumption from over 2400 charging stations in Washington State and San Diego, California. The different data sets allow the influence of time-of-use pricing on the time-series models to be explored. Weekday, weekend, and near-term and long-term models are developed and analyzed. The best performing near-term weekday models are (2, 0, 0) × (0, 1, 1)24× (1, 0, 0)120for Washington State and (2, 0, 0) × (1, 1, 0)24× (0, 0, 1)48for San Diego. Applications of the seasonal ARIMA models to aggregate EV charging station load forecasting and creation of synthetic time-series at different penetration levels are discussed. [ABSTRACT FROM PUBLISHER]

This paper estimates the daily kWh responses on a working weekday of 1326 single-family-home residents who voluntarily participated in a residential optional dynamic pricing (RODP) pilot in the winter-peaking coastal province of British Columbia (BC) in western Canada. Based on the pilot's operation in November 2007-February 2008, we estimate that the kWh reduction in the peak period of 4-9 pm on a working weekday sans an in-home display (IHD) is: (a) 2.2% to 4.4% at time-of-use tariffs with peak-to-off-peak price ratios of 2.0 to 6.0; and (b) 4.8 to 5.3% at critical peak pricing tariffs with peak-to-off-peak price ratios of 8.0 to 12.0. The IHD approximately doubles these estimated peak kWh reductions. As BC residents already have smart meters with an IHD function, these findings recommend exploring the use of a system-wide RODP program to improve the BC grid's system efficiency. [ABSTRACT FROM AUTHOR]

Retail energy providers (REPs) can employ different strategies such as offering demand response (DR) programs, participating in bilateral contracts, and employing self-generation distributed generation (DG) units to avoid financial losses in the volatile electricity markets. In this paper, the problem of setting dynamic retail sales price by a REP is addressed with a robust optimization technique. In the proposed model, the REP offers price-based DR programs while it faces uncertainties in the wholesale market price. The main contribution of this paper is using a robust optimization approach for setting the short-term dynamic retail rates for an asset-light REP. With this approach, the REP can decide how to participate in forward contracts and call options. They can also determine the optimal operation of the self-generation DG units. Several case studies have been carried out for a REP with 10,679 residential consumers. The deterministic approach and its robust counterpart are used to solve the problem. The results show that, with a slight decrease in the expected payoff, the REP can effectively protect itself against price variations. Offering time-variable retail rates also can increase the expected profit of the REPs. [ABSTRACT FROM AUTHOR]

Time-of-use price will be an important demand side management method as a supplement of administrative strategy, which could encourage the electric vehicles to change the charging behaviors. In this paper, for parking garage charging stations with photovoltaic power generation, a pricing model is proposed to encourage different consumers to participate in demand response by providing them with a list of price plans. Firstly, we introduce the system structure of the photovoltaic-assisted charging station. Afterwards, the pricing and dispatch model for the charging station is proposed, aiming to achieve maximum profit. The formulated objective for this model is to maximize the profit of selling electricity to electric vehicles and back to the grid, feed-in-tariffs revenue and parking revenue or to minimize charging station's cost of purchasing electricity, while considering various electric vehicles demands. The decision variables for charging station include the time-of-use price, the rebate on parking fee and the charging/discharging power. To solve this model, the distributed alternating direction method of multipliers is adopted to determine the operational strategies of charging stations via local decision making and limited communication between adjacent charging stations. Finally, according to the simulation results, the optimal time-of-use pricing strategy for charging stations can play an important role in shaving the peak loads. Besides, the interconnected charging stations coordinated operation can enhance the overall system operational flexibility and reliability. [ABSTRACT FROM AUTHOR]

This paper investigates an energy-conscious unrelated parallel machine scheduling problem under time-of-use (TOU) electricity pricing scheme, in which the electricity price varies throughout a day. The problem lies in assigning a group of jobs to a set of unrelated parallel machines and then scheduling jobs on each separate machine so as to minimize the total electricity cost. We first build an improved continuous-time mixed-integer linear programming (MILP) model for the problem. To tackle large-size problems, we then propose a two-stage heuristic. Specifically, at the first stage, jobs are assigned to machines aiming at minimizing the total electricity cost under the preemptive circumstance. At the second stage, the jobs assigned to each machine are scheduled using an insertion heuristic. Computational results on a real-life instance for turning process and random test instances demonstrate that the proposed MILP approach is able to solve small-size problems while the two-stage heuristic is appropriate for large-size problems. The case study for turning process also reveals that the proposed optimization approaches can contribute to cleaner production. [ABSTRACT FROM AUTHOR]

This study investigates the impact of single-phase plug-in electric vehicles charging on increasing the rate at which center-tapped distribution transformers experience aging. Distribution transformer aging is investigated considering varying rooftop solar photovoltaic generation penetration rates. Monte Carlo methods are used to probabilistically estimate the transformer’s loss of life considering the effect of time-of-use (TOU) pricing. The results of applying the proposed method have revealed that plug-in battery electric vehicle charging impact on both transformer aging and neutral current is largest in the case that vehicles charge based on time-of-use pricing methods. Further application has shown that while rooftop solar photovoltaic generation reduces transformer aging, no significant reduction in neutral current is observed. [ABSTRACT FROM AUTHOR]

Most integrated iron and steel corporations have built on–site power plants (OSPPs) to reduce their purchased electricity and thus to decrease the overall electricity cost. Due to their large quantities and easy access, the byproduct gases generated in the steel production process are the main fuels used for the OSPPs. The introduction of time–of–use (TOU) electricity pricing in the steel industry has made it possible to decrease electricity costs through an optimal collaboration between the energy storage equipment (gasholders) and OSPPs. In this paper, a byproduct gas scheduling model based on mixed–integer linear programing (MILP) considering the TOU electricity pricing is proposed. In this model, Pareto optimality and fuzzy sets were used to find the best compromise solution for two conflicting objectives: achieving the gasholder stability and reducing the electricity purchasing cost. In addition, the influence of the operation load on the boiler efficiency was considered to improve the model accuracy. The results show that the optimisation can achieve better peak–valley shifting of the electricity generation and decrease the electricity purchasing cost by 29.7% with improved gasholder stability. Optimisation increased the overall boiler efficiency by 3.3%, indicating that the byproduct gases are effectively and efficiently used. The sensitivity analysis results indicate that the peak–valley shifting of the electricity generation improves with increasing peak–valley price rate (PVR) at the expense of decreasing the overall gasholder stability. [ABSTRACT FROM AUTHOR]

Energy storage is unique in that it can provide multiple services. This feature raises cost-recovery issues for storage, due to the combination of competitive markets and ratebased cost recovery used in many power systems today. This hybrid regulatory paradigm relies on classifying assets as providing competitively prices or unpriced services and handling cost recovery based on that classification. Some recent regulatory precedents suggest that storage developers must choose between classifying their assets as providing competitively priced or unpriced services. In the former case, storage costs must be recovered through the market. If an asset is classified as providing only unpriced services, costs can be recovered through the ratebase. This regulatory design can hamper cost recovery for storage and may lead to inefficient storage investment and use. We propose an alternate solution whereby storage-capacity rights are auctioned to third parties that use their rights for priced or unpriced services. Storage-capacity rights disentangle storage cost recovery from the regulatory treatment of its end use. We formulate the storage-capacity auction model and demonstrate how to efficiently price storage-capacity rights. We show that the revenues earned by the storage owner through the auction equals the imputed marginal value of storage capacity, as revealed by the market bids. [ABSTRACT FROM AUTHOR]

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing, since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multiobjective production scheduling algorithm is developed to obtain Pareto optimal solutions, and then TOPSIS based multicriteria decision-making is performed to recommend an optimal solution to facilitate field operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid. [ABSTRACT FROM AUTHOR]