Search Results (39)

The coordination of microgrid (MG) and distribution is an emerging trend for future development. This paper proposes an uncertainty feasible region (UFR) analysis method based on outer approximation cutting (OAC) under the coordination of MG and distribution. Firstly, an optimal economic dispatch scheduling is obtained. It serves as the basis for the intraday analysis of UFR. Drawing on the concepts of robust optimization, a method for determining the intra-day UFR is proposed. Subsequently, the problem is transformed using duality theory by identifying umbrella constraints, ultimately linearizing the problem to enable its solution by commercial software. In the intra-day analysis of the feasible region, the interactive power deviation between the MG and the upper-level grid (ULG) is allowed, which is represented by an interactive power deviation factor (IPDF). Different factors represent varying sizes of controllable resources, and a larger factor positively affects the size of the feasible region, and the volume is used to represent the size of the feasible region. The UFR defined in this paper provides a theoretical basis for renewable energy consumption capacity corresponding to the day-ahead dispatch scheduling. The effectiveness of the proposed method is validated by simulation results in a typical MG scenario. Full article

With the continuous increase in the number of electric vehicles (EVs) and the rapid development of demand response (DR) technology, the power grid faces unprecedented challenges. A two-stage dual-level dispatch optimization model of multiple virtual power plants based on a Stackelberg game is proposed in this paper. In the day-ahead stage, a two-layer optimization scheduling model is established, where the EV layer optimizes its actions for maximum comprehensive user satisfaction, while the VPP layer optimizes its actions for minimal operating costs and interaction power, determining the scheduling arrangements for each distributed energy resource. In the intraday stage, a Stackelberg game model is constructed with the distribution network operator (DNO) as the leader, aiming to maximize profits, and the VPP as the follower, aiming to minimize its own operational costs, with both parties engaging in a game based on electricity prices and energy consumption strategies. In the simulation case study, the effectiveness of the constructed model was verified. The results show that the model can effectively reduce user costs, thereby increasing the comprehensive satisfaction of EV users by 20.7% and reducing VPP operating costs by 13.37%. Full article

In order to enhance the carbon reduction potential of a park, a low-carbon economic dispatch method applicable to zero-carbon parks is proposed to optimize the energy dispatch of the park at multiple timescales; this is achieved by introducing a flexible response mechanism for source–load bilaterals, so as to achieve low-carbon, economic, and efficient operation. First, a park model that accounts for the energy flow characteristics and carbon potential distribution of the energy hub is established. Then, based on the flexible operation of energy supply equipment and multi-type integrated demand response, the flexible response mechanism of source–load bilaterally and the multi-timescale scheduling framework are proposed; the mechanisms of source–load coordination and electricity–carbon coupling are analyzed in depth. Finally, with the objective of optimal system operation economy, the optimal scheduling model is established for three timescales, namely, day-ahead, intraday, and real-time scheduling. The equipment output and demand response are optimized step by step according to the source–load prediction information and scheduling results at each stage. The simulation results show that the proposed model can effectively utilize the source and load resources to participate in scheduling and can effectively reduce carbon emissions while ensuring the energy supply demand of the park, realizing the low-carbon, economic operation of the system. Therefore, this study provides a new theoretical basis and practical solution for the optimal dispatch of energy in zero-carbon parks, which helps to promote the development of a low-carbon economy. Full article

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system. Full article

As the penetration of renewable energy increases, the distribution grid faces great challenges in integrating large amounts of distributed energy sources and dealing with their output uncertainty. To address this, a multi-time scale optimal dispatch method based on model predictive control is proposed, including a day-ahead stage and an intra-day rolling stage. In the day-ahead stage, to fully utilize the flexibility of variable speed pumped storage hydropower, the generating/pumping phase modulation condition is considered, not just generating or pumping. Day-ahead optimal dispatch is established with the objective of minimizing the operation economy and node voltage deviation of the distribution network. In the intra-day rolling stage, model predictive control with finite time domain rolling optimal dispatch is used to replace the traditional single-time section optimal dispatch, considering the forecast data of wind, photovoltaic (PV), and load within the finite time domain, so that can respond in advance to smooth the generator output. At the same time, the uncertainty problem of the distribution network is solved effectively by rolling optimization and feedback correction of model predictive control. In order to consider the daily operating capacity balance of energy storage in the intra-day stage, the capacity imbalance penalty is added to the intra-day rolling optimization objective function, so that the energy storage capacity tries to track the results of the day-ahead optimization, achieving the long-term development of energy storage. Simulation analysis proves the feasibility and effectiveness of the proposed method. The proposed method enhances the generation–load–storage coordinated dispatching ability, effectively improving the distribution network’s capability to respond to fluctuations of renewable energy. Full article

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day’s internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison. Full article

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management. Full article

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. Full article

Wind and solar power curtailment and the difficulty of peak regulation are issues that urgently need to be addressed in the process of China’s new electric power system. Enterprises with captive power plants (ECPPs) are large-capacity power consumers and producers, with significant optimization and adjustment potential on both the supply and demand sides. This paper aims to promote the active participation of ECPPs in grid supply–demand regulation and proposes an optimization model for the power generation and consumption of ECPPs based on a day-ahead, intra-day two-stage dispatching model. First, targeting demand response scenarios, mathematical models for analyzing the potential of ECPPs to participate in power grid supply–demand regulation are proposed. Then, an optimization model for ECPP generation and consumption with load regulation is established, and a two-stage dispatching model is proposed to fully mobilize the regulation flexibility of ECPPs. Finally, a robust dispatching model considering price uncertainty is established based on information gap decision theory. The case results show that ECPPs can reduce the curtailment rate in a region by approximately 9%, alleviate the peak pressure of the power grid, reduce carbon emissions by 1373.55 tons, and promote low-carbon development for themselves. Meanwhile, considering price uncertainty strengthens the risk resistance capability of ECPPs and provides a basis for their willingness to participate in supply–demand regulation. Full article

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method’s effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate. Full article

Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively. Full article

The anti-peaking characteristics of a high proportion of new energy sources intensify the peak shaving pressure on systems. Carbon capture power plants, as low-carbon and flexible resources, could be beneficial in peak shaving applications. This paper explores the role of carbon capture devices in terms of peak shaving, valley filling, and adjustment flexibility and constructs a virtual energy storage model utilizing various flexible loads on the demand side. Also, it proposes a joint peak shaving strategy involving carbon capture devices and virtual energy storage. Considering the predictive error characteristics of wind power and load across different time scales, this study establishes a day-ahead and intraday two-stage rolling regulation peaking model based on carbon capture power plants and virtual energy storage to fully leverage the diverse response speeds and peak shaving capabilities of various types of flexible loads. Initially, the model calculates the net load curve after implementing a demand response system, aiming to minimize the load peak–valley difference. Subsequently, within the intraday period, it seeks to minimize system operating costs by precisely allocating peak shaving resources as per demand, thus aiming for economic efficiency while ensuring the system’s peak shaving capability. Full article

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets. Full article

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost. Full article

Under the premise of continuously increasing the grid-connected capacity of new energy, the fluctuation and anti-peak shaving characteristics of wind power have always constrained the development of green power systems. Considering the characteristics of power system flexibility resources, this paper introduces a two-stage regulation approach for power systems with enhanced source–load flexibility. In the day-ahead stage, an advanced peak regulation transformation is employed, leveraging the combined heat storage device of conventional thermal power units to enhance their peak regulation capability. Additionally, the Energy Intensive Load (EIL) is integrated into the regulation system. A two-level coordinated optimization model is developed, incorporating wind power integration and dispatching power allocation, with the aim of optimizing wind power integration and achieving the optimal allocation of dispatching power. In the intra-day stage, the connection of wind plants and energy storage devices is utilized to minimize the wind power fluctuations and improve the control ability over wind power variations. Compared with traditional methods, the wind power consumption in Scenario 1 and Scenario 2 increases by 2741.1 MW/h and 2478.5 MW/h respectively. Furthermore, the inclusion of an energy storage device in the intra-day stage significantly reduces the wind power fluctuations, maintaining a stable fluctuation rate within ±1%. Therefore, this method can effectively improve the level of wind power consumption and reduce the impact of real-time fluctuations on the power system. Full article