Dispatch Method Research Articles

Distributionally robust optimal dispatching method of integrated electricity and heating system based on improved Wasserstein metric

This paper proposed a distributionally robust optimal dispatching (DROD) method of integrated electricity and heating system based on an improved Wasserstein metric, for dispatching problems to manage the risk from uncertain wind power forecasted errors and operating characteristics of electric vehicles (EVs). The ambiguity set employed in the distributionally robust formulation for wind power forecasted error is the Wasserstein ball centered at the empirical distribution and an adjustable uncertainty set of EV operating characteristics is proposed in this model to quantify the uncertainty of EVs more flexibly and intuitively. The proposed framework minimizes the generating cost, EV operating cost, and the expected generation units adjustable cost under the worst-case distribution in the uncertainty set. To improve the computational efficiency of the proposed DROD model, an improved Wasserstein metric based on extreme scenarios DROD (WMES-DROD) model is proposed to construct the ambiguity set of wind power forecasted error. The comparison between the Wasserstein metric-based DROD and WMES-DROD indicates that the proposed WMES-DROD model can obtain optimal results with less time. The out-of-sample analysis with the IEEE 118-bus test system confirms the excellent robustness and computational superiority of the proposed model.

Distributed Economic Control Method for Energy Storage Unit with Random Time Delay

The operation cost waste in the charge and discharge process cannot be ignored for islanded microgrids with energy storage units. Different from the economic dispatch methods focused on the power pricing and bid coefficient in the tertiary control layer, this paper designed a power management algorithm for the economic operation of energy storage units in the secondary control layer. The strategy is devoted to minimizing the total charge power cost of energy storage units while satisfying the constraint of power balance. Meanwhile, the maximized allowable delay bounds were derived to guarantee the communication network reliability when against random time delays. The system stability was analyzed by the Lyapunov functions. Finally, the numerical simulations on the modified IEEE 30 feeder model verify the superiority of the provided algorithm.

Stability and Distributed Optimization for AC Microgrid Considering Line Losses and Time Delay

With the development of distributed renewable generations, a large number of distributed generations (DGs) are connected to the microgrid. Therefore, distributed cooperative algorithms are more suitable for optimal dispatch of the microgrid than centralized algorithms. This paper proposes a novel distributed cooperative control method for optimal dispatch of microgrids, considering line losses and time delay. First, the optimization model of the microgrid considering line losses is established, and optimality conditions are obtained. Second, a novel distributed optimization method considering line losses is proposed, where the primary control is to achieve optimal dispatch, and the secondary control is to achieve frequency regulation. Third, the stability of the system under time delay is analyzed, and the robust stability conditions are obtained. Finally, simulation results verify the the effectiveness of the proposed method.

Synergetic Scheduling Energy and Reserve of Wind Farms for Power Systems with High-Share Wind Power

With wind and other variable renewable energy (VRE) generation gradually becoming the main power source, it is difficult to ensure the flexible and secure operation of the system by completely relying on conventional sources to balance the uncertainty and volatility of VRE. The increase of VRE penetration leads to the shortage of operation reserves, and wind power is allowed to participate in reserve services. However, the output of wind power is uncertain, and how to ensure the reliability and sustainability of wind power reserves is still an open question. A robust energy and reserve dispatch method considering the flexibility of wind power is proposed to solve this problem. To effectively measure the output and reserve margin of wind power under uncertain scenarios, (1) the grid connection coefficient of wind turbine generators (WTGs) is defined, and the uncertainty set considering active wind power curtailment is established; and (2) based on two kinds of WTG control methods, the reserve model of wind farms is established. The model links the reserve capacity between the base scenario and uncertain scenarios through the reserve coefficient, which ensures that the wind power reserve determined in the base scenario is still deliverable in any uncertain scenario. The robust scheduling model determines the optimal dispatching and reserve plan of conventional units and wind farms by balancing the system operation economy and the schedulable reserve of wind power. The column-and-constraint generation algorithm is used to solve the model. The effectiveness of the proposed model is verified by the illustrative results of the IEEE normal system.

An equivalent model‐based asynchronous dispatch method for clusters of flexible distributed energy resources

AbstractWith the increasing number of flexible distributed energy resources, it is difficult for the distribution network to control various devices directly. Therefore, managing and dispatching the resources in a cluster way is required. This article proposes an equivalent model‐based asynchronous dispatch framework, which is composed of three layers, the distribution network, clusters, and distributed energy resources. With this framework, economic dispatch is carried out with the equivalent models of clusters, simplifying the dispatch problem of the distribution network. The numerical test results show that the proposed method can significantly reduce the calculation time and maintain high accuracy in the economic dispatch problem.

Energy Dispatch for CCHP System in Summer Based on Deep Reinforcement Learning

Combined cooling, heating, and power (CCHP) system is an effective solution to solve energy and environmental problems. However, due to the demand-side load uncertainty, load-prediction error, environmental change, and demand charge, the energy dispatch optimization of the CCHP system is definitely a tough challenge. In view of this, this paper proposes a dispatch method based on the deep reinforcement learning (DRL) algorithm, DoubleDQN, to generate an optimal dispatch strategy for the CCHP system in the summer. By integrating DRL, this method does not require any prediction information, and can adapt to the load uncertainty. The simulation result shows that compared with strategies based on benchmark policies and DQN, the proposed dispatch strategy not only well preserves the thermal comfort, but also reduces the total intra-month cost by 0.13~31.32%, of which the demand charge is reduced by 2.19~46.57%. In addition, this method is proven to have the potential to be applied in the real world by testing under extended scenarios.

A novel prediction and control method for solar energy dispatch based on the battery energy storage system using an experimental dataset

The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE% =1.68 %.

Hierarchical AGC Dispatch With Detailed Modeling of Energy Storage System Behaviors

The key challenge for automatic generation control (AGC) dispatch lies in the contradiction between the detailed modeling required for optimal dispatch and the tight calculation time. The current method includes (1) the heuristics method that allocates real-time commands based on certain rules (fast but nonoptimal) and (2) the proactive dispatch method with a detailed AGC dispatch model (relatively optimal but the forecast error exists). The development of renewables and the prevalence of energy storage systems (ESSs) with costly degradation calls for combining the advantages of heuristics and proactive methods in AGC dispatch. With this in mind, a hierarchical AGC dispatch method is proposed. A proactive AGC dispatch model embedding state of charge (SOC) recovery is established to make the best use of regulating resources while avoiding the degradation of ESSs. A uniform SOC model considering comprehensive ramping circumstances of ESSs is proposed with low model complexity. A theoretical analysis demonstrates the desired modeling accuracy. To compensate for the forecast error, real-time AGC dispatch is considered. The dead band and forced SOC recovery of ESSs are used to alleviate their degradation. Time-domain simulations based on practical data demonstrate that the proposed method can improve the system frequency performance while obviously reducing the degradation of ESSs.

A fast data-driven optimization method of multi-area combined economic emission dispatch

To provide more feasible schemes for power system optimization management and operation, the interconnection of power grids in different areas is inevitable. Naturally, the multi-area combined economic/emission dispatch (MACEED) problem becomes a more important decision problem. However, as the dimensions of MACEED problems increase, existing studies may not obtain feasible scheduling decisions in a suitable time. On this background, MACEED problems are transferred into computational expensive problems. In order to solve high-dimensional, large-scale MACEED problems, a data-driven surrogate-assisted approach is proposed. First, a feature engineering-based support vector regression surrogate model is proposed to replace the traditional objective functions in high-dimensional MACEED problems. Second, knowledge distillation is applied as a freezing and fine-tuning mechanism for the improved support vector regression surrogate models, which significantly reduces the time required to build surrogate models. Third, an improved non-dominated sorting genetic algorithm-III is proposed to obtain feasible solutions to high-dimensional MACEED problem. The proposed algorithm enhances the convergence and diversity of optimal solutions. The effectiveness of the proposed data-driven approach is demonstrated through simulations of a four-area 40-unit test system under different constraints.

Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.

Carbon constrained economic dispatch of a microgrid system based on different grid pricing strategies considering uncertainty

AbstractMicrogrids can be considered a reduced power system network wherein the generation, transmission, and distribution of power occurs within a limited geographical area. They are employed to use the maximum penetration of renewable energy sources (RES). Microgrid also has advantages like reduction of transmission losses and costs pertaining to such losses. The objectives of economic dispatch, emission dispatch, penalty‐factor (PPF) based combined economic emission dispatch (CEED) and environmental constrained economic dispatch (ECED) are evaluated in this paper. A low‐voltage microgrid system for three different scenarios is studied. A novel, robust and powerful hybrid swarm‐intelligence optimization algorithm is used, which is based on amalgamating the features of conventional gray‐wolf optimizer, sine‐cosine and crow search algorithm. Due to the use of RES, the active power production cost and carbon emission of the microgrid system were reduced to approximately 3% for all the objectives. More than 15% reductions in generation costs were obtained throughout the study when the time‐of‐usage electricity market pricing strategy was used instead of the fixed pricing strategy. Also, results show that the ECED methods of economic emission dispatch provided better‐compromised solutions than the PPF‐based CEED method. Finally, non‐parametric statistical analysis is used to analyze and validate the quality of the projected novel technique, which is measured against the outcomes found with algorithms used in the state‐of‐art literature.

Economic dispatch for microgrids with communication delays and flexible load

The consensus algorithm is widely used in the microgrid, but it is affected by the communication of distributed generators. If the communication delay is serious, it can affect the system’s performance and cause the system’s instability. To ensure the stability of the microgrid under communication constraints, aiming at the extensive penetration of the flexible loads in the microgrid, the transmission delay of the communication network is considered, this paper proposes a microgrid distributed economic dispatch method based on the consensus algorithm under communication constraints. By coordinating the information exchanges between the generator and the total load demand, the flexible load and output of the generator are optimized in the system. Compared with the traditional consensus control strategy, this strategy can ensure that the system has higher stability and convergence speed under the same delay. Finally, the IEEE-14 node system is used to verify the effectiveness and correctness of the proposed algorithm.

Economic dispatch of thermal and PV powers: Frequency maintenance, smoothing effect based statistic model and peak-cut operation

Smart grid allows the penetration of a high percentage of power from renewable energy sources (RES). The intermittent nature of RES makes accurate prediction of its output power impossible, and the prediction uncertainty is inevitable. This imposes a great challenge to the operation of smart grid. This paper focuses on photovoltaic (PV) generation and proposes a robust stochastic approach for the grid operation. First, we show that the uncertainty can be significantly reduced by the smoothing effect of PVs and build a simplified statistical model for the prediction of the total PV power, together with a bound of prediction uncertainty. After that, a criterion for the frequency maintenance is derived, which relates the allowable bound of frequency deviation to that of the PV power uncertainty. Then, a robust stochastic economic dispatch of the thermal power is formulated based on this criterion. Further, we propose an optimal PV dispatch method based on a peak-cut operation to maximize the PV output while guaranteeing the frequency performance. Finally, the operation performance is analyzed in detail on an annual basis by simulations using actual solar radiation data. It is revealed that the frequency quality is maintained with a 5% increase in fuel cost and an 11% reduction of PV power for a penetration ratio over 30%, compared with the case w/o frequency maintenance.

Multilevel optimization of economic dispatching in active distribution network based on ADMM

With the continuous improvement of the penetration rate of renewable energy and the continuous integration of advanced network control technology and measurement equipment, the traditional distribution network is developing into an active distribution network (ADN) with the characteristics of flexible scheduling control, high user interaction, and high energy utilization. This article fully considers the economy of the overall operation of the distribution network, and proposes a hierarchical optimization economic dispatch method for active distribution networks based on the alternating direction multiplier method (ADMM). Firstly, a hierarchical optimization scheduling model of active distribution network is established with the goal of minimizing the overall operating cost of the distribution network. The alternating direction multiplier method algorithm is decomposed into upper and lower layers to solve. The upper layer is optimized with the goal of minimizing the overall operating cost of the distribution network, and the lower layer considers the distribution network. The distributed photovoltaic and energy storage units connected to the internal nodes of the network are optimized with the goal of minimizing the local energy storage operation cost and power purchase cost. The upper and lower layers, through the exchange of limited boundary information, iterate each other until the convergence conditions are met, and the optimal solution is obtained. Finally, a design example is tested to verify the effectiveness and feasibility of the proposed scheduling method.

A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem

The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t-test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem.

Assessing animal welfare impact of fourteen control and dispatch methods for house mouse (Mus musculus), Norway rat (Rattus norvegicus) and black rat (Rattus rattus).

Population control of the house mouse (Mus musculus), Norway rat (Rattus norvegicus) and black rat (Rattus rattus) is common practice worldwide. Our objective was to assess the impact on animal welfare of lethal and non-lethal control methods, including three dispatch methods. We used the Sharp and Saunders welfare assessment model with eight experts scoring eleven control methods and three dispatch methods used on the three species. We presumed the methods were performed as prescribed, only taking into account the effect on the target animal (and not, for example, on non-target catches). We did not assess population control efficacy of the methods. Methods considered to induce the least suffering to the target animal were captive-bolt traps, electrocution traps and cervical dislocation, while those with the greatest impact were anticoagulants, cholecalciferol and deprivation. Experts indicated considerable uncertainty regarding their evaluation of certain methods, which emphasises the need for further scientific research. In particular, the impact of hydrogen cyanide, chloralose and aluminium phosphide on animal welfare ought to be investigated. The experts also stressed the need to improve Standard Operating Procedures and to incorporate animal welfare assessments in Integrated Pest Management (IPM). The results of our study can help laypeople, professionals, regulatory agencies and legislators making well-informed decisions as to which methods to use when controlling commensal rodents.

A cloud edge computing method for economic dispatch of active distribution network with multi-microgrids

• A method of cloud edge computing is proposed to solve the economic dispatch. • Multi-agent deep reinforcement learning is adopted to realize cloud edge computing. • The cloud server and each edge server respond to each agent. • The proposed method can protect data privacy. In view of the risks and challenges of privacy data leakage and the communication burden in the traditional economic dispatch for active distribution network with multi-microgrids, this paper proposes a cloud edge computing method for economic dispatch of active distribution network with multi-microgrids. In this method, the cloud server is responsible for the calculation of active distribution network, and each edge server is in charge of the calculation of its own microgrid. The multi-agent deep reinforcement learning is employed to realize cloud edge collaborative computing, where each edge server and cloud server corresponds to an agent. Through case analysis, the reliability of the cloud edge computing method is confirmed. The simulation results show that the proposed method can provide a high-quality solution for economic dispatch of active distribution network with multi-microgrids on the premise of protecting data privacy and reducing the communication burden.

Low-Carbon Economic Dispatch Method for Integrated Energy System Considering Seasonal Carbon Flow Dynamic Balance

In order to solve the problems of excessive carbon emissions and environmental pollution caused by the current carbon trading policy, this paper breaks the traditional annual carbon trading mechanism and proposes an integrated energy system (IES) optimal dispatching method considering the seasonal carbon trading mechanism. Firstly, in order to ensure the dynamic balance of the multi-energy flow in the IES, the carbon flow equivalent to the electricity-gas-heat energy flow is introduced into the energy hub model, and the multi-energy flow energy hub model is established. Secondly, an optimized-stepped carbon trading mechanism is formulated to ensure internal carbon balance and restrain carbon emissions to prevent the annual carbon emissions settlement of the IES from exceeding the standard. Finally, according to the energy supply demand of the IES in different seasons, a seasonal carbon trading mechanism is formulated, which comprehensively considers carbon emissions and economics to optimize dispatch. The impact of the optimized-stepped carbon price and seasonal dispatch on carbon emissions and economics is compared. The cost of using the optimized-stepped carbon price is reduced by at least 14.50%, and carbon emissions are reduced by at least 2.54%. Under guaranteeing the same carbon emission quota, the IES net-benefit is increased by 6.8%.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.

Stochastic adaptive-service level agreement-based energy management model for smart grid and prosumers.

The growing issue of demand-supply management between the prosumers and the local energy market requires an efficient and reliable energy management model. The microlayers, such as prosumers, energy districts, and macro players, namely retail dealers and wholesale dealers play a pivotal role in achieving mutual benefits. The stochastic nature of renewable energy generation in energy districts requires an effective model that can contemplate all stochastic complexities. Therefore, this paper proposes a mutual trade model between energy districts and smart grid to authorize the prosumers for mutual energy transactions under the stochastic adaptive-service level agreement. Moreover, multiple smart contacts are developed between the stakeholders to design adaptability and stochastic behavior of wind speed and solar irradiance. The real-time adaptations of the stochastic adaptive-service level agreement are based on technical beneficial feasibility and achieved through stochastic and adaptive functions. The optimized solution based on a genetic algorithm is proposed for the energy cost and energy surplus of prosumers and output parameters of the mutual trade model (grid revenue). In the context of mutual benefits associated with balanced demand and supply, the economic load dispatch and simplex method maximization are used for optimized demand-supply energy management. Moreover, the effectiveness of the proposed adaptive and stochastic mutual trade model is validated through simulation and statistical analysis.