Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

In this article, we study the electricity time-of-use (TOU) tariff for an electricity company with stochastic demand. The electricity company offers the flat rate (FR) and TOU tariffs to customers. Under the FR tariff, the customer pays a flat price for electricity consumption in both the peak and non-peak periods. Under the TOU tariff, the customer pays a high price for electricity consumption in the peak period and a low price for electricity consumption in the non-peak period. The electricity company uses two technologies, namely the base-load and peak-load technologies, to generate electricity. We derive the optimal capacity investment and pricing decisions for the electricity company. Furthermore, we use real data from a case study to validate the results and derive insights for implementing the TOU tariff. We show that in almost all the cases, the electricity company needs less capacity for both technologies under the TOU tariff than under the FR tariff, even though the expected demand in the non-peak period increases. In addition, except for some extreme cases, there is essentially no signicant reduction in the total demand of the two periods, although the TOU tariff can reduce the demand in the peak period. Under the price-cap regulation, the customer may pay a lower price on average under the TOU tariff than under the FR tariff. We conduct an extensive numerical study to assess the impacts of the model parameters on the optimal solutions and the robustness of the analytical results, and generate managerial implications of the research findings. [ABSTRACT FROM AUTHOR]

Eco-friendly energies have recently become a popular topic. Given this trend, we predict that a large number of electric vehicles (EVs) will be widely used. However, EVs need to be connected to a power system for charging, thereby causing severe risks, such as rapid increase of demand power. Therefore, in this study, we analyze the effects of EV charging on demand power, which depend on different charging schemes, namely, dumb charging, off-peak charging, time-of-use (ToU) price-based charging. For practical analysis, we conduct simulations by considering the actual power system and driving patterns in South Korea. Simulation results show that the ToU price-based charging scheme exhibits better performance in terms of demand power over the other charging schemes. [ABSTRACT FROM AUTHOR]

With the increasing energy price, the rapid growth of electricity demand and severe challenges for sustainable development, energy-efficient scheduling is becoming more and more important for power-intensive manufacturing industry, especially for batch production companies. This paper investigates a bi-objective single machine batch scheduling problem with non-identical job sizes, the time-of-use (TOU) electricity prices, and different energy consumption rates of the machine. The first objective is to minimize the makespan and the second is to minimize the total energy costs, by considering both the machine utilization and the economic cost. The problem is formulated as an integer programming model. Then, an exact ε -constraint method is adapted to obtain the exact Pareto front. To deal with large scale problems, based on decomposition ideas, two heuristic methods are developed to obtain approximate Pareto fronts. Computational experiments on randomly generated instances show the effectiveness of the methods. A study case of a real-world glass manufacturing company is also conducted to show that the proposed methods are promising for practical usages. [ABSTRACT FROM AUTHOR]

According to the parking features of electric vehicles (EVs) and load of production unit, a power supply system including EVs charging station was established, and an orderly discharging strategy for EVs was proposed as well to reduce the basic tariff of producer and improve the total benefits of EV discharging. Based on the target of maximizing the annual income of producer, considering the total benefits of EV discharging, the electric vehicle aggregator (EVA) and time-of-use (TOU) price were introduced to establish the optimization scheduling model of EVs discharging. Furthermore, an improved artificial fish swarm algorithm (IAFSA) combined with the penalty function methods was applied to solve the model. It can be shown from the simulation results that the optimal solution obtained by IAFSA is regarded as the orderly discharging strategy for EVs, which could reduce the basic tariff of producer and improve the total benefits of EV discharging. [ABSTRACT FROM AUTHOR]

The paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m 3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%. [ABSTRACT FROM AUTHOR]

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price. [ABSTRACT FROM AUTHOR]

Indirect load control (ILC) is a method by which the customer determines load reduction of electricity by using a price signal. One of the ILCs is a time-of-use (TOU) tariff, which is the most commonly used time-varying retail pricing. Under the TOU tariff, the customer can reduce the energy cost through an energy storage system (ESS). However, because this tariff is fixed for several months, the ESS operation does not truly reflect the wholesale market price, which could widely fluctuate. To overcome this limitation, this paper proposes an incentive pricing method in which the load-serving entity (LSE) gives the incentive pricing signal to the customers with ESSs. Because the ESS charging schedule is determined by the customer through ILC, a bilevel optimization problem that includes the customer optimization problem is utilized to determine the incentive pricing signal. Further, the bilevel optimization problem is reformulated into a one-level problem to be solved by an interior point method. In the proposed incentive scheme: (1) the social welfare increases and (2) the increased social welfare can be equitably divided between the LSE and the customer; and (3) the proposed incentive scheme leads the customer to voluntarily follow the pricing signal. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to design feasible Time-of-Use (ToU) tariffs for domestic customers from flat rate tariffs by clustering techniques. The method is dedicated to designing the fundamental window patterns of ToU tariffs rather than optimising exact prices for each settlement period. It makes use of Gaussian Mixture Model clustering technique to group half-hour interval flat rate tariffs within a day into clusters to determine ToU tariffs. Two groups of ToU are designed following the variations in energy prices and system loading demand respectively. With a number of price-oriented and load-oriented ToU tariffs, the investigation is further carried out to explore the effects of these ToU tariffs on domestic demand response (DR), especially in terms of energy cost reduction and peak shaving. The DR in this paper is assumed to be enabled by household storage battery and the objective of the DR in response to each ToU tariff is to minimise the electricity bills for end customers and/or mitigate network pressures. An example study in the UK case is also carried out to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

A new ‘econological scheduling’ model combining the economic and ecological aspects of a multi-part multi-machine setup operating under a time-of-use tariff is presented. The operating speed of the machines and the frequency of operating speed change are allowed to vary, and the peak load and energy consumption during a shift is estimated using discrete event simulation. The electricity cost and environmental impact for a target production quota are simultaneously minimized using a multi-criterion meta-heuristic optimization. The proposed model is demonstrated via a case study on a manufacturing unit producing parts using machining and welding operations. A comparison among econological, economic, and ecological approaches and the underlying dynamics of scheduling under a time-varying electricity tariff are presented as one of several strategies for enabling a manufacturing system to be more eco-friendly without substantially increasing the electricity cost. [ABSTRACT FROM AUTHOR]

Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and Time-of-Use (ToU) pricing refer to programs which offer incentives to the end users who curtail their energy use during times of peak demand. This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers. The proposed model optimally schedules the electricity consumption of different household appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that the proposed method efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) pricing serves as a cost-effective way to realize electricity demand response, which aims at relieving peak demand. Customer participation is critical to the success of TOU pricing programs. To fulfill the potential of such programs, customers must be able to access electricity tariffs and understand their terms. This paper reports a survey of 43 TOU pricing programs targeting industrial customers offered by U.S. utilities. This work is inspired by and complements the Federal Energy Regulatory Commission survey of demand response in the electric power industry, highlighting the interpretation of key pricing components and specific characteristics of TOU tariff sheets collected from public sources. The case studies examine various industrial scenarios to predict electricity cost savings when customers are facing the transition from flat rates to TOU pricing. The analysis results show that the cost savings vary enormously, ranging from −72.0% to +82.6%, depending on specific utility programs and switching strategies involved. Such information is useful for customers to determine whether to participate in a TOU pricing program. Key findings and implications for industrial customers, utilities, and regulatory agencies are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a storage system has been considered to be added to a private electricity facility in order to reduce the electricity bill. This kind of system could make sense with a time-of-use (TOU) tariff (with two or three periods of different electricity price) or a real-time pricing (RTP) tariff (which can vary hourly depending on wholesale market), where each day, electricity is bought from the AC grid during off-peak hours (at low price) to store energy, and during on-peak hours (at high price), the storage supplies the whole load or a part, avoiding the purchase of expensive electricity from the AC grid. A new optimisation model to obtain the best storage size and control of the charge–discharge process under RTP tariff has been developed, minimising the net present cost of the system. This methodology allows the electricity consumer to evaluate the profitability of adding storage. A case study of battery storage with a hypothetical hourly RTP tariff based on the Spanish electricity market pool of 2013 is shown. Considering the cost of batteries per kW h cycled expected in the near future (0.045 €/kW h cycled ), even the optimal storage size with the optimal control is not profitable compared to the system without storage. A much lower battery cost (0.0225 €/kW h cycled ) would be needed so that the storage system could be economically profitable. Considering a stretched RTP hourly pricing tariff (hourly prices higher than the average of each day multiplied by 1.3 and the lower ones by 0.7), the storage system with the costs expected in the near future (0.045 €/kW h cycled ) is profitable. We conclude that an hourly tariff that takes the base of the Spanish electricity market pool is not adequate to stimulate this technology, as the difference between maximum and minimum prices during the day is too low to compensate the capital cost plus replacement, operation, and maintenance of the storage system. In other countries, with much higher difference between minimum and maximum electricity price of the day, present battery cost could make it profitable. [ABSTRACT FROM AUTHOR]

Highlights: [•] Households respond to demand based time-of-use electricity distribution tariffs. [•] They respond by cutting demand in peak hours and shifting demand from peak hours. [•] Decreases in electricity consumption are a by-product of demand response programs. [•] Demand response is less marked in rental and particularly condominium apartments. [•] Demand response in single-family homes does not decrease in the long term. [ABSTRACT FROM AUTHOR]

Fast and elitist nondominated sorting generic algorithm (NSGA2) is an improved multiobjective genetic algorithm with good convergence and robustness. The Pareto optimal solution set using NSGA2 has the character of uniform distribution. This paper builds a time-of-use (TOU) pricing mathematical model considering actual constraint conditions and puts forward a new method which realizes multiobjective TOU pricing optimization using NSGA2. A variety of objective TOU pricing schemes can be provided for decision makers compared with traditional method. Furthermore, the multiple attribute decision making theory is applied in processing the Pareto optimal solution set to calculate the optimal compromise price scheme. The simulation results have shown that the TOU pricing scheme determined by the method proposed above can achieve a better effect of clipping the peak load to fill the valley load. Consequently, the study in this paper is innovative and is a successful exploration of coordinating the relation of various objective functions concerned in TOU pricing optimization problem. [ABSTRACT FROM AUTHOR]

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters' behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models. [ABSTRACT FROM AUTHOR]

A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power. [ABSTRACT FROM AUTHOR]

Demand for electricity varies throughout the day, increasing the average cost of power supply. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a game-theoretic approach to optimize TOU pricing strategies (GT-TOU). We propose models of costs to utility companies arising from user demand fluctuations, and models of user satisfaction with the difference between the nominal demand and the actual consumption. We design utility functions for the company and the users, and obtain a Nash equilibrium using backward induction. In addition to a single-user-type scenario, we also consider a scenario with multiple types of users, each of whom responds differently to time-dependent prices. Numerical examples show that our method is effective in leveling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. An increase in social welfare measure indicates improved market efficiency through TOU pricing. [ABSTRACT FROM PUBLISHER]

Abstract: In the electricity industry, the processes through which consumers respond to price signals embedded in tariffs by changing their consumption patterns is generally referred to as demand response. In such a context, consumers are offered an opportunity to maximize the surplus derived from electricity usage by actively scheduling consumption over time periods with potentially different energy prices. The objective of this work is to analyze the role of correlation in prices of successive periods over which consumption is to be scheduled, in a demand response context. We use robust optimization techniques to propose an optimization model for consumption scheduling when prices of different periods are highly correlated, and also suggest approaches to correctly incorporate real-world correlated price data to this model. Positive results from quantitative case studies indicate that it is of great importance to employ a solution approach that correctly models correlation among prices when scheduling consumption. [Copyright &y& Elsevier]

Abstract: Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets. We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system. [Copyright &y& Elsevier]

Abstract: This research study examines the peak energy reduction potential and avoided cost from using an automatic thermostat set point control strategy in a residential home which responds to the price variation in a time-of-use tariff. Twelve benchmark homes, composed of three different home sizes in four different geographical locations, are each evaluated under twelve unique control scenarios with systematically varied peak period length, rate ratio, and set point offset. All time-of-use tariffs are designed to be cost neutral with the existing flat rate tariff assuming the same base case operating scenario. Historical real time prices for 2008 and regional specific construction features were used. The total savings potential as well as the additional benefit from increased set point offsets were found to be highly dependent on the climate. For a particular location, savings on a percentage basis were very consistent across home sizes for similar control strategies, meaning the same tariff may be applied to all residential customers without disadvantaging customers in smaller homes. Finally, the utility cost savings varied from $225 to $9 per home with many time-of-use tariffs resulting in more money given to the homeowner than actually saved by the utility. [Copyright &y& Elsevier]

As both a regulator and an academic, Fred Kahn argued that end-use electricity consumers should face prices that reflect the time-varying marginal costs of generating electricity. This has been very slow to happen in the US, even in light of recent technological advances that have lowered costs and improved functionality for meters and automated demand response technologies. We describe these recent developments and discuss the remaining barriers to the proliferation of time-varying electricity pricing. [ABSTRACT FROM AUTHOR]

Abstract: Peak electricity demand has been an issue in the Desert Southwest region of the US, due to extreme summer temperatures. To address this issue, a consortium was formed between the University of Nevada, Las Vegas, Pulte Homes, and NV Energy. An energy efficient residential community was developed by the team in Las Vegas with approximately 200 homes to study substation-level peak reduction strategies. A summer peak reduction of more than 65%, between 1:00PM and 7:00PM, compared to code standard housing developments is the targeted goal of the project. Approximately 50 homes are already built and some are occupied. The energy performances of the homes have been monitored and are presented in this paper. Several peak electric load reduction strategies such as energy efficiency in buildings, roof integrated photovoltaics (PV) and direct load control have been applied. Though all the homes in the developed community are installed with 1.8kWp PV systems, the orientation of the PV system depends on the building orientation. Focus of this paper is to find the impact of PV orientation on the peak load from a building. In addition, different time-of-use (TOU) energy pricing options are offered by the local electrical utility company. Hence it is important to find an optimal pricing option for each building. A computer model has been developed for one of the homes in the new development using building energy simulation code, ENERGY-10. Calculations on the PV orientations have shown that a south and 220° (i.e. 40° west of due south) orientations are economically optimal for homes enrolled to flat electricity pricing schedule and time of use pricing respectively. As predicted by the simulations, the energy efficiency methods in the home have decreased the total annual energy by 38% compared to a code standard home of the same size. Further, the energy efficiency methods in the building coupled with a 220° oriented PV and 1.1°C increase in thermostat temperature for three hours (from 3:00 to 6:00PM) reduced the peak energy demand by 62% compared to a code standard building of the same size. Battery storage is the other option that will be considered in the project at a later time which is expected to carry the project well beyond its peak reduction goals. [Copyright &y& Elsevier]

Abstract: In this article we develop an extension of the affine jump–diffusion modeling framework and use it to build an intuitive and tractable model of an energy price complex. The development is motivated by the need to model prices of electricity while capturing their dependence on the price of other energy commodities. Such a model is essential for valuing a range of typical derivatives traded in the electricity markets: cross-commodity spread options, cross-location spread options, fuel-switching powerplants, etc. We give an approximate pricing method for these derivatives together with precise error bound estimates. [Copyright &y& Elsevier]

Automatic meter reading (AMR) is an application implemented by power utilities which is becoming increasingly important in the utilities sector. It could be included in the more general environment of demand side management, which satisfies the need for more improved communications. AMR offers a better financial return due to the inclusion of other applications. Although there are several international standards which address AMR, most deployments are currently using proprietary systems. This fact may reflect that current standards do not fully cover this area. Since the power network reaches all energy meters, a convenient way to deploy AMR is to use the power lines as a transmission medium. The present-day situation is that there is mounting pressure to deploy AMR systems in a bid to provide better management of the energy demand in a bid to reduce global warming and create a more sustainable electrical system in our environmentally conscious society. This paper is focused on analyzing different standards and specifications related to AMR over powerline communications (PLC). Furthermore, two of them are chosen, implemented, and simulated using the OPNET modeler. The main goal of the implemented applications is to obtain comparative results of the two most known standard applications in AMR over PLC. [ABSTRACT FROM AUTHOR]

This paper constitutes – to our best knowledge – the first econometric analysis on stock market effects of the EU Emission Trading Scheme (EU ETS). Our results suggest that EU Emission Allowance (EUA) price developments matter to the stock performance of electricity firms: EUA price changes and stock returns of the most important European electricity corporations are shown to be positively related. This effect does not work asymmetrically, so that stock markets do not seem to react differently to EUA appreciations in comparison to depreciations. The carbon market effect is shown to be both time- and country-specific: It is particularly strong for the period of EUA market shock in early 2006, and differs with respect to the countries where the electricity corporations analysed are headquartered. Stock market reactions to EUA volatility could not be shown. [Copyright &y& Elsevier]

This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules will be submitted by GENCOs to the ISO for approval before implementation. The objective of a GENCO is to consider financial risks when scheduling its midterm maintenance outages. The GENCO also coordinates its proposed outage scheduling with short-term unit commitment for maximizing payoffs. The proposed model is a stochastic mixed integer linear program in which random hourly prices of energy, ancillary services, and fuel are modeled as scenarios in the Monte Carlo method. Financial risks associated with price uncertainty are considered by applying expected downside risks which are incorporated explicitly as constraints. This paper shows that GENCOs could decrease financial risks by adjusting expected payoffs. Illustrative examples show the calculation of GENCO's midterm generation maintenance schedule, risk level, hourly unit commitment, and hourly dispatch for bidding into energy and ancillary services markets. [ABSTRACT FROM AUTHOR]

Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. Market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants' behaviors. Various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing (LMP) spot markets. Available correlated data also have to be selected to improve the short-term forecasting performance. In this paper, fuzzy inference system (FIS), least-squares estimation (LSE), and the combination of FIS and LSE are proposed. Based on extensive testing with various techniques, LSE provides the most accurate results, and FIS, which is also highly accurate, provides transparency and interpretability. [ABSTRACT FROM AUTHOR]

Suppliers in competitive electricity markets regularly respond to prices that change hour by hour or even more frequently, but most consumers respond to price changes on a very different time scale, i.e., they observe and respond to changes in price as reflected on their monthly bills. In this paper, we examine mixed complementarity programming models of equilibrium that can bridge the speed of response gap between suppliers and consumers yet adhere to the principle of marginal cost pricing of electricity. We develop a computable equilibrium model to estimate ex ante time-of-use (TOU) prices for a retail electricity market. It is intended that the proposed models would be useful 1) for jurisdictions (e.g., Ontario) where consumers' prices are regulated, but suppliers offer into a competitive market, 2) for forecasting forward prices in unregulated markets, and 3) in evaluation and welfare analysis of the policies regarding regulated TOU pricing compared to regulated single pricing. [ABSTRACT FROM AUTHOR]

We consider interruptible electricity contracts issued by an electricity retailer that allow for interruptions to electric service in exchange for either an overall reduction in the price of electricity delivered or for financial compensation at the time of interruption. We provide a structural model to determine electricity prices based on stochastic models of supply and demand. We use stochastic dynamic programming to value interruptible contracts from the point of view of an electricity retailer, and describe the optimal interruption strategy. We also demonstrate that structural models can be used to value contracts in competitive markets. Our numerical results indicate that, in a deregulated market, interruptible contracts can help alleviate supply problems associated with spikes of price and demand and that competition between retailers results in lower value and less frequent interruption. [ABSTRACT FROM AUTHOR]