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Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing. Finally, the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS. By simulating a real small-scale RIES, the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

PurposeRapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.Design/methodology/approachBased on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.Findings(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.Originality/valueThis paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.HighlightsElectricity pricing and capacity of energy storage power stations in an uncertain electricity market.Investment strategy of energy storage power stations on the supply side of wind power generators.Impact of pricing method on the investment decisions of energy storage power stations.Impact of pricing method, energy storage investment and incentive policies on carbon emissions.A two-stage wind power supply chain including energy storage power stations.

In recent times, transportation electrification has been recognized as one of the key solutions to accelerate global GHG emission reductions. As the electric vehicle industry grows faster, plug-in electric vehicles (PEV) are expected to be the most dominant load in the utility sector in less than a decade. Regular charging of the battery energy storage system (BESS) is a mandate for the continued operation of the vehicle, and the PEVs are connected to the utility to charge. Since PEVs are mobility loads, predicting the interconnection of these mobility loads in the utility network for recharging is a major challenge. The intermittent connection of mobility loads to the grid for charging leads to an unpredictable increase in electricity demand and other grid-related issues. Optimal scheduling of PEV charging would conquer the grid-related issues and provide financial benefits to the users. In this paper, an intelligent charge scheduling technique of PEV charging for both residential and commercial charging stations using the heuristic algorithm is proposed and discussed. The primary objective of the algorithm is to achieve the minimization of PEV charging costs by implementing an interrupted charging schedule. The proposed algorithm is tested by conducting exhaustive simulation studies under several conditions for PEVs with different power ratings for residential and commercial charging scenarios. The time-of-use pricing (ToUP) system is adopted as a tariff system in this paper. A detailed comparison of the unscheduled algorithm, the modified placement algorithm (MPA) and proposed heuristic technique-based charge scheduling is carried out through simulation studies. A detailed cost analysis for charging the PEVs with the selected charge scheduling techniques for various conditions is conducted and cost minimization by implementing the proposed charging scheme is validated.

This paper examines the effects of time-of-use (TOU) pricing on distributed renewable energy (DRE) investment for a non-power generating firm. We develop an electricity consumption cost-minimization model by considering the intermittent generation as well as the firm’s electricity consumption. It has been found that implementing full retail prices compensation for the surplus renewable electricity is probably not good as it may lead to DRE over-investment. Moreover, we find that the firm’s optimal investment strategy is not necessarily sensitive to the price signal of TOU pricing (i.e., the ratio of peak to off-peak price). Particularly, when the service-level difference in meeting a firm’s electricity consumption between peak and off-peak periods by adopting DRE technology is above a critical threshold in relation to the peak time, a strong price signal will not promote the firm’s optimal DRE capacity investment. This paper yields a policy insight that “getting the time right” may be more important than “getting the price right” in terms of enabling DRE investment for TOU pricing design.

Economic inefficiency can be caused by time-invariant retail electricity prices because they do not reflect variations in the cost of providing electricity during the day. Time-of-use (TOU) pricing—higher electricity prices during peak hours and lower electricity prices during off-peak hours—is by far the most common way to achieve more efficient levels of electricity consumption through reducing peak demand. The empirical evidence of the effectiveness of TOU pricing is sparse in the commercial and industrial sectors and there is no consensus in the literature on the statistical significance and magnitude of the effects. Applying a quasi-experimental design, this study evaluates an ongoing experiment of voluntary business TOU pricing plan by a major utility company in the Phoenix metropolitan area. Using the nearest-neighbor matching method, we identify control customers for the voluntary participants of the business TOU pricing. From difference-in-differences analysis, we find a statistically significant reduction in peak-hour electricity demand in response to the TOU pricing. We also find that there is no conservation effect, meaning that the total level of electricity consumption does not change under the TOU pricing.

To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, considering photovoltaic power uncertainty, energy storage dynamics, and time-of-use pricing. To address coupled scheduling and energy management challenges, a three-stage bilevel collaborative optimization framework is proposed, enhancing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to develop a Collaborative MOPSO (CMOPSO). The improved algorithm features a four-layer encoding mechanism with energy factors, chaotic mapping for better global search, and adaptive mutation for population diversity. Validation using the Brandimarte benchmark demonstrates the algorithm’s robustness. Specifically, comparative experiments reveal that the proposed strategy significantly outperforms the traditional scheduling mode. While maintaining a similar makespan, the proposed method reduces production costs by 44.3% and carbon emissions by 29%. Simulations confirm that the method effectively shifts tasks to low-price periods and leverages photovoltaic energy during peak hours, supporting the manufacturing industry’s green transition.

The high energy consumption and substantial electricity costs of cloud data centers pose significant challenges related to carbon emissions and operational expenses for service providers. The temporal variability of electricity pricing in real-world scenarios adds complexity to this problem while simultaneously offering novel opportunities for mitigation. This study addresses the task scheduling optimization problem under time-of-use pricing conditions in cloud computing environments by proposing an innovative task scheduling approach. To balance the three competing objectives of electricity cost, energy consumption, and task delay, we formulate a price-aware, multi-objective task scheduling optimization problem and establish a Markov decision process model. By integrating prioritized experience replay with a multi-objective preference vector selection mechanism, we design a dynamic, multi-objective deep reinforcement learning algorithm named TEPTS. The simulation results demonstrate that TEPTS achieves superior convergence and diversity compared to three other multi-objective optimization methods while exhibiting excellent scalability across varying test durations and system workload intensities. Specifically, under the TOU pricing scenario, the task migration rate during peak periods exceeds 33.90%, achieving a 13.89% to 36.89% reduction in energy consumption and a 14.09% to 45.33% reduction in electricity costs.

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. The work was based on multi-regional peak and valley data, and we selected Lanzhou, Guiyang, Beijing, Guangzhou, Shanghai, and Nanjing as typical representatives for systematic validation and analysis. Our findings were as follows: (1) The model demonstrated excellent convergence and stability during the electricity price optimization process, particularly under flat-rate price conditions. This model effectively avoided local optima traps and enhanced global search capability, achieving the dual goals of rapid convergence and high stability, and showed exceptional optimization efficiency and adaptability; (2) building upon its optimization performance, the model further improved the uniformity and diversity of the solution distribution, ensuring robustness and flexibility in global search ability. Moreover, by dynamically adjusting the price function and multi-level evaluation system, the model significantly optimized price elasticity, time-of-use pricing regulation efficiency, energy consumption paths, and the operational stability of the distribution network. The model exhibited high resilience and fine-grained control capabilities in the complex electricity market; (3) finally, based on the optimized electricity price strategy derived from training, the model reduced electricity costs and price volatility. Moreover, its superior performance in economic benefits and market adaptability was comprehensively validated through high-precision power consumption forecasting. We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.

Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system.