Optimal Dispatch Research Articles

Stable power supply system consisting of solar, wind and liquid carbon dioxide energy storage

Renewable energy is becoming the main subject of energy consumption with the accelerating transformation of energy structure. The renewable power supply systems sourced by wind and solar energies have attracted wide attention as they are of great significance to regions that are rich in renewable energy. In this study, the stable power system consisting of solar, wind and liquid carbon dioxide energy storage is proposed for the sake of meeting user electricity load. Thermodynamic and economic performance of the proposed systems with different application scenarios is analyzed and some interesting findings are summarized. The round trip efficiency and energy density of the liquid carbon dioxide energy storage system are 58.34 % and 23.41 kWh/m3, respectively. The start hour of dispatch can cause obvious influence on the energy storage capacity and there is an optimal dispatch start time to achieve the minimum energy storage capacity. The overall energy efficiency of energy storage-aided power system including solar and wind powers is much higher than that of the single sourced system. The energy efficiency of the solar-wind-LCES system is 94.61 % while it is only 80.31 % and 76.29 % for the wind-LCES and solar-LCES systems, respectively. The introduction of the liquid carbon dioxide energy storage into the renewable power supply system can greatly reduce the electricity purchasing investment.

Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties

With the increasing popularity of wind and solar generation, the stable and economic operation of the community microgrid system become more challenging. This paper is concerned with the robust scheduling and real-time control of demand response (DR) air conditioning (AC) loads for community microgrid. Firstly, a two-stage robust scheduling model coupled by the day-ahead dispatch and real-time dispatch is established by considering the uncertainties of wind/photovoltaic power generation and loads. Moreover, the incorporation of multiple interval uncertain sets is introduced to characterize the uncertainties related to wind power, photovoltaics, and load, thereby reducing the conservativeness of the model. Secondly, the real-time control of AC loads by adjusting the on/off actions and temperature setpoints with limited impact on user comfort so as to track the optimized electricity consumption curve. Finally, the simulation results show the effectiveness and robustness of the proposed optimization dispatch and real-time control algorithm.

Optimized Dispatch of Regional Integrated Energy System Considering Wind Power Consumption in Low-Temperature Environment

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of “heat for electricity” and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system’s consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments.

An Optimal Dispatch Method of Integrated Electricity and Gas Systems Incorporating Hydrogen Injection Flexibility

Hydrogen enriched compressed natural gas provides a promising way of decarbonizing integrated electricity and gas systems. Considering the flexible characteristics of hydrogen enriched compressed natural gas, this paper proposes an optimal dispatch method for integrated electricity and gas systems incorporating hydrogen injection. A hydrogen blending transmission model is developed for hydrogen enriched compressed natural gas. The dynamic characteristics and the line pack are captured considering the impacts of hydrogen injection on pipeline transmission and line pack parameters. From the overall system operation aspect, a detailed energy flow model of hydrogen enriched compressed natural gas involving pipeline, compressor, and nodal gas flow balance is established incorporating the coupling relationship with hydrogen volume fraction. This model allows for the consideration of the impacts of time-varying hydrogen volume fractions on the optimal dispatch of systems. A novel estimation model is proposed to effectively determine the hydrogen volume fraction, which considers the impacts of line pack on hydrogen enriched compressed natural gas. Based on the proposed energy flow model, the line pack and the flexible adjustment of hydrogen volume fraction are aggregated into the flexible characteristics of hydrogen enriched compressed natural gas. A low-carbon dispatch method incorporating these flexible characteristics is developed to decarbonize integrated electricity and gas systems. Case studies on two test systems are presented to validate the effectiveness and applicability of the proposed model and method. The simulation results demonstrate that the accommodation levels of renewable energy and green hydrogen injection can be largely enhanced.

Price Formation and Multi-Market Synergistic Strategies for Renewable Electricity under the Concept of Life Community

Abstract This paper constructs the price formation mechanism of renewable electricity with three pricing mechanisms, namely, system marginal price (SMP), zonal marginal price (ZMP) and nodal marginal price (LMP), on the basis of electricity price location signal. Through the objective function of the constructed optimal dispatch model of renewable energy power, we calculate the minimum operating cost of each power plant under multi-market synergy and use the C&CG column constraint method to solve the price fluctuation caused by the uncertainty of wind and light. Finally, the multi-market synergy rate of the model is estimated by combining the 500 kV, 300 kV and 200 kV unified wind turbines invested by a provincial grid company in China. The results show that the multi-market coordination coefficients α of wind power and photovoltaic projects are 0.1148 and 0.2849, respectively, and the renewable electricity decreases from 0.355 Yuan/kWh to 0.298 Yuan/kWh in 2025 under the optimal dispatch model of electricity. This paper has certain theoretical value and practical reference significance for future renewable energy power price reform.

A non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning for the optimal dispatch of the combined cooling, heating, and power system with photovoltaic thermal collector

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario.

A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs

An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information.

Comprehensive multi-benefit planning of sustainable interconnected microgrids

A comprehensive stochastic planning model with multi-objective, integrated sizing and scheduling strategies for distributed energy resources is fabricated and implemented for interconnected grid-tied microgrids in the present work. The optimum capacities of the renewable and dispatchable distributed generators, together with battery energy storage systems in the interconnected microgrids, are ascertained using grey wolf optimizer in the fuzzy domain with the objectives of minimizing the yearly expenses, emissions and energy loss, and maximizing the yearly monetary benefit acquired due to the deferral of network upgrades. The optimal dispatch of multi-energy resources is intended to satisfy price-sensitive, multi-energy demands economically and sustainably under the influence of a price-based demand response strategy. The proposed multi-microgrid planning framework, which is hinged on probabilistic power flow with the probabilistic modelling of renewable and load uncertainties, is targeted to benefit multiple entities, including the utility and microgrid prosumers. The effectiveness of the proposed formulation is demonstrated by the mitigation of the annual expenses, emissions, and energy loss by 51%, 70%, and 56%, respectively, and the deferral of the need for the relevant upgrades by 12 years. Moreover, the performance of the integrated resource allocation and scheduling model is also endorsed using environmental sustainability factors.

Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer

Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer

An optimal dispatch schedule of EVs considering demand response using improved MACD algorithm

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

Analysis of the Implementation of Virtual Power Plants and Their Impacts on Electrical Systems

The increasing penetration of Distributed Energy Resources (DERs) in Distribution Systems (DSs) has motivated studies on Virtual Power Plants (VPPs). However, few studies have jointly assessed the sizing and economic attractiveness of VPPs from the entrepreneur’s perspective and the potential benefits and impacts on power systems while maintaining the scope to DSs. This study proposes a methodology for sizing VPPs and simulating their economic optimal dispatch and economic attractiveness with a focus on the entrepreneur’s viewpoint. In addition, it also evaluates VPPs’ potential benefits and impacts on a DS or Transmission System (TS) while considering the interface between the Distribution System Operator (DSO) and the Transmission System Operator (TSO). The methodology employs optimization to minimize the Net Present Cost (NPC) of the project, in relation to sizing the DERs, and to obtain the economic optimal dispatch of the BESSs that comprise the VPP. Moreover, a power flow analysis and probabilistic reliability assessment are used to evaluate the benefits and impacts on the power system. The methodology was applied to a case study involving Photovoltaic (PV) systems and Battery Energy Storage Systems (BESSs) used by aggregated medium voltage consumers, which configure Technical Virtual Power Plants (TVPPs) participating in Demand Response (DR) via incentives, with a network model of the Brazilian National Interconnected System (SIN) adapted from the 2030 Ten-Year Energy Expansion Plan (PDE) of the Energy Research Office (EPE), along with data from the Geographic Database of the Distribution Utility (BDGD). The results indicate the economic attractiveness of DERs according to the premises adopted and indicate improvements in TS reliability indexes with the possibility of TVPPs’ dispatch after transmission contingencies.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Stochastic optimal dispatch of offshore-onshore regional integrated energy system based on improved state-space approximate dynamic programming

The offshore-onshore regional integrated energy system (OORIES), which includes offshore gas production platforms and wind farms, along with the onshore gas-fired combined cooling, heating, and power supply campus, can effectively improve economic and environmental benefits by the complementary and coupling operation of multiple energies. In this paper, multi-energy coupling elements and multiple energy networks in the OORIES are modeled in detail. Considering the stochastic fluctuation of the offshore gas source and wind power output, a stochastic optimal dispatch (SOD) model of OORIES with multiple energy storages, including associated gas storage, hydrogen and heat storage tanks is proposed. Furthermore, a carbon trading mechanism is introduced to ensure the environmental benefits. An improved state-space approximate dynamic programming (SSADP) algorithm is proposed to solve the SOD model. By using the approximate value functions, the multi-period SOD model is decoupled into a series of single-period optimization models which are recursively solved period by period. The approximate analytical expression of the value function concerning states of each period is obtained based on the polynomial approximation, and combined with the transition probability matrices of stochastic quantities, an accurate and efficient solution for each single-period optimization model is obtained. A case study on an OORIES, and compared with the scenario-based algorithm, chance-constrained programming algorithm and traditional SSADP algorithm, demonstrates the effectiveness and high efficiency of the proposed method.

Optimal dispatch of a mobile storage unit to support electric vehicles charging stations

AbstractAs transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. Mobile Energy Storage Systems (MESS) offer versatile solutions, aiding distribution systems with reactive power, renewables integration, and peak shaving. An MESS can be utilized to serve electric vehicles (EVs) in different parking lots (PLs), in addition to supplying power to the grid during overloads. The task of multiple stationary units can be achieved using MESS at a relatively lower cost. This paper proposes an MESS owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The main objective of the proposed approach is to dispatch the MESS in conjunction with optimal EVs’ charging coordination to minimize operational costs and address the extra demand of PLs. A mixed‐integer nonlinear programming (MINLP) problem is formulated and solved. Considering electricity price variations and EVs uncertainties, three different case studies are performed to highlight the efficiency and success of the proposed approach. The simulation results in a huge reduction in the total operation cost and the savings reach up to 27.51% in comparison with the base case.

Chance-constrained optimal dispatch of integrated energy systems based on data-driven sparse polynomial chaos expansion

In integrated energy systems, stochastic variations of different energy types of loads and the increasing penetration of renewable energy generation have resulted in considerable uncertainties. These uncertainties pose significant challenges to the economics and safe operation of integrated energy systems. Conventional deterministic methods of optimal dispatch overlook the effects of uncertainties, while stochastic optimization, though accounting for uncertainties, often yields conservative solutions that may adversely affect the economic operations. Chance-constrained optimization can effectively deal with uncertainties and provide more flexibility in balancing operational risks and benefits by expanding the feasible region. Hence, this paper proposes a chance-constrained optimal dispatch method for integrated energy systems and employs data-driven sparse polynomial chaos expansion method to enhance solving efficiency. First, the proposed chance-constrained optimization aims to ensure the optimal economic operation with an affordable security confidence level that balances safety and economics compared with deterministic and stochastic optimizations. The introduced data-driven sparse polynomial chaos expansion method enables the fast computing of the output response using only historical data, i.e., without knowledge pertaining to the distribution functions. Moreover, an improved iterative verification structure is proposed, which further improves the convergence speed and accuracy. Finally, the advantages and feasibility of the proposed method are verified using a test case and compared with those of deterministic and stochastic optimization. Results show that the proposed method successfully reduces the operational cost and controls violation probability.

Exploration and application of new distribution system technology for large-scale distributed distribution network digital service access

Abstract Digital technology is leading fundamental innovation and development, and digitalization represents a great opportunity for the energy industry to take off and develop. In this paper, digital and intelligent technologies are used to integrate, analyze, and manage distribution network equipment information. Secondly, a new smart distribution terminal design is proposed and modeled using the IEC 61850 standard to monitor the status of smart distribution grid management equipment. Through the active distribution system structure, based on the decoupled PCC, a model is constructed for large-scale distributed optimal dispatching. Finally, two scenario experiments are taken as examples to simulate and analyze the distributed co-dispatch of the new distribution system. The results show that the operation reward interval of scenario 2 is [194.43, 205.44] when the decomposition convergence criterion is set to 0.05 and the threshold value of the second-order cone relaxation accuracy is set to 10−5. The novel distribution system proposed in this paper is able to achieve optimal decision-making that satisfies the distribution network operation constraints, while the multi-microgrid seeks to maximize its benefits.

Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

Optimal Generation Dispatch in Electrical Microgrids Based on Inertia Markets as a Solution to Frequency Stability

This paper addresses a crucial omission in the traditional approach to solving the classic economic dispatch problem within microgrids featuring renewable energy sources—the often-neglected frequency disturbances arising from reductions in system inertia. To remedy this, we present an innovative economic dispatch model empowered by nonlinear optimization (NLP), incorporating stringent minimum inertia constraints essential for ensuring system stability over a 24-h horizon. Our approach involves a comprehensive exploration of the intricate relationship between system inertia and frequency stability, culminating in the seamless integration of these inertia constraints into the economic dispatch model. To validate the practicality of our model, we present two distinct scenarios: a base case representing conventional dispatch methodologies and an alternative case that considers the imposition of inertia restrictions. These scenarios are rigorously tested and implemented using the CICGRE TF C6.04 test system. Employing the powerful GAMS platform alongside the NPL model, we successfully solved the dispatch problem. Our results underscore the significance of maintaining system inertia within the 1.54-s threshold proposed by our model, showcasing a tangible reduction in generation costs as a direct outcome of this enhanced approach to economic dispatch. This research advances the understanding of microgrid management and offers a practical solution to enhance system stability and economic efficiency in renewable-energy-powered microgrids.

Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties

Grid scale battery integration plays an important role in renewable energy integration and the formation of smart grid. To mitigate the problems of insufficient frequency response and peak regulation capacities faced by modern power grids with high wind energy uptake, a day-ahead optimization dispatch strategy considering operational risks is proposed in this study. In the day-ahead dispatch model, generation units and a large-scale battery energy storage station (LS-BESS) are coordinated to participate in multi-type frequency control ancillary services (FCASs). For optimal performance, scheduling in different timescales and the complementarity between power and energy types of requirements are coordinated, with various service uncertainties considered. Then the conditional value-at-risk theory is utilized to achieve the optimal mix of multiple resources for day-ahead regulation reserves, to realize the minimum operation cost. To tackle the uncertainty of wind power over a large timescale, a robust optimization (RO) approach based on the budget uncertainty set is employed, which considers robustness and economy. This can help avoid over-conservation of the standard RO and enhance the applicability of the decisions made. Simulation studies and comparison analysis of multiple schemes verify the effectiveness of the proposed optimal day-ahead dispatch strategy, which also demonstrate that a LS-BESS participating in multiple FCASs for day-ahead dispatch can help realize secure, reliable, and economic power grid.

Optimal dispatch model for aviation-fuel vehicles in refuelling operations

Taiwan Taoyuan international airport has been devoted to becoming a green airport in recent years, aiming to reduce carbon dioxide emissions (CDEs) from ground handling services. Refuelling operations play a pivotal role in airport operations and, given the short and frequent nature of flight takeoffs and landings, demand substantial work. Different aircraft types and flight lengths require varying refuelling operations, and the inefficient and unrealistic dispatch of aviation-fuel vehicles can lead to flight delays. In this study, time–space network flow techniques were used to formalise the flow of aviation-fuel vehicles in both space and time. Based on the problem characteristics and by adding appropriate constraints, the aim of this work was to minimise CDEs and construct an optimal model for the refuelling operation scheduling of aviation-fuel vehicles. The proposed model was evaluated through a case study, and conclusions and recommendations are presented.