Dispatch Of Power System Research Articles

Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model

The proliferation of demand response programs in the smart grid provides the system operator unique opportunities to reduce the load peak and alleviate network congestions. This paper considers the economic dispatch problem with elastic demands which flexibly respond to the locational marginal prices (LMPs). However, LMP is the dual variable of optimal power flow (OPF) problem and thus is unknown before the OPF problem is solved. Without LMP, the elastic demand is unclear, and the OPF problem cannot be set up. Given this interactive nature, it is difficult to acquire the dispatch strategy as well as the LMP according to the traditional OPF method. This paper thoroughly addresses this problem. Specifically, the limitation of the traditional LMP scheme in the described situation is analyzed. An equilibrium solution may not exist because the demand function and the discontinuous LMP may not have an intersection. To overcome this difficulty, LMP at the discontinuity point is redefined, so that the dispatch problem always has an equilibrium solution. A mixed-integer linear programming model for the economic dispatch problem with LMP-dependent load is proposed, and the equilibrium solution simultaneously offers the dispatch strategy and LMPs. Case studies demonstrate the difficulties of traditional approaches and the effectiveness of the proposed method.

Bi-level Coordinated Optimal Dispatching Method of Power System Considering Renewable Energy Uncertainty and Flexible Load Aggregators

The increase of renewable energy penetration and the diversity of flexible loads in the customer side have posed new challenges to secure and economic dispatching of power system. Based on the concept of load aggregator (LA), we propose a bi-level optimal dispatching model for power system. In order to guarantee the security of power system operation, we propose a bi-level coordinated dispatching method to consider the aggregation of various flexible loads and the uncertainty of renewable energy generation, which can realize efficient and economic dispatch of power system. Numerical results in one test system demonstrate that our proposed dispatching model can reduce the total dispatch cost and LA’s electricity bill simultaneously.

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

Day-ahead residential load forecasting is crucial for electricity dispatch and demand response in power systems. Electrical loads are characterized by volatility and uncertainty caused by external factors, especially for individual residential loads. With the deployment of advanced metering infrastructure, the acquisition of electricity consumptions of multiple residential customers is available. This paper proposes a novel day-ahead residential load forecasting method based on feature engineering, pooling, and a hybrid deep learning model. Feature engineering is performed using two-stage preprocessing on data from each user, i.e., decomposition and multi-source input dimension reconstruction. Pooling is then adopted to merge data from both the target user and its interconnected users, in a descending order based on mutual information. Finally, a hybrid model with two input channels is developed by combining long short-term memory (LSTM) with self-attention mechanism (SAM). The case studies are conducted on a practical dataset containing multiple residential users. Performance of the proposed load forecasting method using data pools of different groups of users as well as different input forms is compared. The effectiveness of input dimension reconstruction and hybrid model is also validated. The overall results demonstrate the superiority of the proposed load forecasting method through comparison with other benchmark methods.

Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models

Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.

An Accelerated Distributed Gradient-Based Algorithm for Constrained Optimization With Application to Economic Dispatch in a Large-Scale Power System

In this article, we consider a convex optimization problem which minimizes the sum of local agents' cost functions subject to certain local constraints. Besides, both the local cost function and local constraints are only known by the local agent itself. To solve this problem, a new accelerated distributed gradient-based algorithm is proposed, which is inspired by the “momentum” phenomena in nature and aims to accelerate the convergence speed of conventional distributed gradient algorithms. Sufficient conditions for the stepsizes and the acceleration gains are derived to ensure the convergence of the proposed algorithm. Furthermore, based on this proposed fast distributed algorithm, a new decentralized approach is proposed to solve economic dispatch problem, especially for a large-scale power system. Based on the idea of virtual agent, it is proved that this decentralized algorithm is equivalent to the original fast distributed gradient method. Several case studies implemented on IEEE 30-bus, IEEE 118-bus power systems, and a large-scale power system consisting of 1000 generators are conducted to validate the proposed method.

Sensitivity Analysis of Optimal Power Dispatch for All-Electric Ship

Shipping industry is reforming and changing fast, as the International Maritime Organization (IMO) works towards air pollution prevention and ship-owners pursue more efficient operation of their ships. Formerly, propulsion and electric load dispatch in ship power system is implemented proportionally with respect to nominal power of prime movers and generators respectively. Additionally, integrated full electric propulsion, optimal real-time dispatch to ship generators and the integration of new systems, such as energy storage systems, shaft generators etc. could have gained a wider application. In this paper the optimal dispatch for ship power system based on Lagrange method is presented comparing the classic and all-electric ship design. The developed method is applied to an ten years old Ro-Ro (roll on / roll off) passenger ship and an analytical sensitivity analysis is occurred out with respect not only to technical characteristics of the ship, such as fuel kind, propulsion chain factors, but also voyage characteristics, such as ship speed, route length etc.

Research on Location and Layout of Electric Vehicle Charging Facilities Based on Power System Dispatch

In order to enable the limited charging stations in the road network to meet as many demands as possible, and to give full play to the advantages of electric vehicles in changing the energy consumption structure and reducing greenhouse gases, a dualobjective charging station optimization problem is proposed. The paper uses the shortest path and the next-shortest path to describe the traffic flow factors in the traffic network. The goal is to maximize the traffic flow intercepted by the charging station, minimize the investment cost of the charging station and minimize the node voltage offset, and establish electric vehicle charging Station multi-objective planning model. The results prove the effectiveness of the proposed method and provide decision support for enterprises to scientifically and reasonably select the location of charging stations.

Optimal Scheduling of Pumped Storage in Power System with Large-scale Photovoltaic Based on Carbon Emissions Trading

In order to improve the photovoltaic penetration of the power system, an optimal scheduling model of pumped storage system with large-scale photovoltaic based on carbon trading is proposed in this paper. Firstly, based on the analysis of low-carbon economy, the stepped carbon emission trading mechanism is introduced into the economic dispatch of power system. And in order to improve the calculating efficiency, an improved K-means clustering algorithm based on maximum and minimum distance criterion is proposed to cluster the photovoltaic power generation scenarios. Then the optimal scheduling model is established. Take the lowest comprehensive operation costs of the system as the objective function, and the operation economy and low carbon of the system are considered. The pumped storage is used as energy storage for peak load shaving in the generation system. At last, an improved IEEE RTS-96 system is taken to finish the case study, and the simulation results show that the model proposed in this paper is reasonable and effective.

Research on Power System Dispatching Automation Technology Based on Big Data

The perfect combination of power dispatch automation system and the analysis and processing of large amounts of data in big data mining technology can meet people’s needs for knowledge; at the same time, the application of mining technology in the power dispatch automation control system realizes the accurate collection and sorting of power data, Has a broad application space and practical application significance. This article introduces the source of grid data in the dispatch automation system, discusses how to extract and analyse the big data in the dispatch automation system efficiently and safely, and provide scientific data support for the economic and stable operation of the power grid and regional planning and development.

A data-adaptive robust unit commitment model considering high penetration of wind power generation and its enhanced uncertainty set

Wind power generation is increasingly penetrating into the power grid, which brings great challenges to the dispatch of power systems. With the popularization of data mining technology, further exploration of the random characteristics of wind power based on the available wind power data can significantly improve the applicability of scheduling decisions. In this paper, a novel data-adaptive robust unit commitment model under high penetration of wind power is proposed, which derives a robust dispatch solution with minimal generation cost while hedging against the worst case in the uncertainty set. Firstly, copula theory is carried out to formulate a joint probabilistic distribution function and capture the correlation of power outputs among multiple wind farms. A large number of wind power scenarios are then generated and the imprecise Dirichlet model (IDM) is applied to derive the boundaries of wind power generation, which helps to construct a more practical polyhedron uncertainty set. Moreover, due to the correlation of adjacent wind farms, the auxiliary variables which determine the fluctuation of wind power have a synchronous trend. Here, the synchronous characteristic is introduced to the enhanced polyhedron uncertainty set by means of the synchronous volatility of the auxiliary variables in adjacent wind farms. Experimental studies are conducted out on a modified IEEE-118 bus system and the obtained scheduling solution is turned out to be superior under wind power uncertainties, which verifies the effectiveness of the proposed data-adaptive robust unit commitment model.

Energy and reserve dispatch with distributionally robust joint chance constraints

We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable spillage with high probability. We solve this problem efficiently using conditional value-at-risk approximations and linear decision rules. Out-of-sample experiments show that this model dominates the corresponding stochastic program without chance constraints that models the effects of load shedding and renewable spillage explicitly.

Effects of intermittent heating on an integrated heat and power dispatch system for wind power integration and corresponding operation regulation

Applying an intermittent heating mode (IHM) in district heating systems (DHSs) during a period of wind power curtailment can effectively decrease the heat demands of the DHSs and increase the operational flexibility of the combined heat and power generation units. Accordingly, it is possible to integrate additional wind power into an integrated heat and power dispatch (IHPD) system. In this study, a new IHPD model with the application of the IHM was built to promote wind power integration. In the IHPD model, to obtain the dynamic hydraulic and thermal conditions of the DHSs and ensure the feasibility of applying the IHM for wind power integration in actual engineering applications, a novel dual hydraulic parameter calibration method was proposed, and an innovative method for determining the optimal heating start-stop times under the IHM was developed. Afterward, the dynamic hydraulic and thermal models of the DHS under the IHM (including corresponding regulation methods) were completely built and embedded into the IHPD model. The effects of the IHM on the IHPD system and optimized hydraulic-thermal characteristics of the DHS were analyzed, based on a real DHS featuring multiple heat sources and looped networks. The results demonstrate that applying the IHM to an IHPD system can significantly increase the operational flexibility of the combined heat and power unit; it can also increase the wind power integration rate from 70.8% to 92.2%, as determined based on a case study. Therefore, the IHM is recommended for application in IHPD systems to promote wind power integration.

Optimal Reactive Power Dispatch With Discrete Controllers Using a Branch-and-Bound Algorithm: A Semidefinite Relaxation Approach

In this paper, a methodology to solve the optimal reactive power dispatch (ORPD) in electric power systems (EPS), considering discrete controllers, is proposed. Discrete controllers, such as the tap position of on-load tap changing (OLTC) transformers and switchable reactive shunt compensation, are optimized by the proposed method. A semidefinite relaxation (SDR) of the ORPD problem and a branch-and-bound (B&B) algorithm have been fully deployed. A new formulation is presented for the OLTC transformers to obtain a connected structure of the semidefinite programming (SDP) matrices. The customized B&B algorithm deals with the discrete nature of the binary control variables. Moreover, in order to enhance the convexification, valid inequalities called lifted nonlinear cuts (NLC) are implemented in the SDR. Additionally, a chordal decomposition technique is used to improve the computational performance. Finally, the B&B algorithm is used to solve the mixed-integer semidefinite programming problem. Several benchmarks have been used to show the accuracy and scalability of the proposed method, and convergence analysis shows that near-global optimal solutions are generated with small relaxation gaps.

Bi-Level Dispatch and Control Architecture for Power System in China Based on Grid-Friendly Virtual Power Plant

Non-synchronous renewable energy sources (RESs) have strong volatility and low inertia, which brings about great challenges on the accommodation of RESs and the security and stability of power systems. This paper proposes a bi-level power system dispatch and control architecture based on the grid-friendly virtual power plant (GVPP), so as to accommodate RESs flexibly and securely. The typical dispatch and control system of the power system in China is presented, and the particular challenges stemming from non-synchronous RESs are analyzed. The functional requirements, concept, and fundamental design of the GVPP are provided, which is distinguished from traditional virtual power plants (VPPs) for its active participation in power system stability control. Based on the cloud platform, a bi-level dispatch and control architecture considering two objectives is established. First, in the inner level, the GVPP operates to promote the accommodation of RESs under normal condition. Then, from the perspective of out-level power systems, GVPPs serve as spinning reserves for power support under contingencies. Besides, the key problems to be solved in the development of the GVPP-based architecture are summarized. Although the architecture is proposed for the power system in China, it can be applied to any power systems with similar challenges.

The effects of carbon emissions trading and renewable portfolio standards on the integrated wind–photovoltaic–thermal power-dispatching system: Real case studies in China

The existing literature demonstrates that the multi-energy combined power-dispatching system is crucial for resolving the mismatch between the installed capacity and the power generation through renewable energy. However, our knowledge of the impact mechanism of climate policies on this system is still limited. To address this gap, cost-accounting models that consider price fluctuation based on carbon emissions trading and renewable portfolio standards in China are proposed and applied to the integrated wind–photovoltaic–thermal power-dispatching system in Xinjiang. The scenarios analysis showed that in terms of reducing emissions, carbon emissions trading is more effective in cases with a high installed proportion of renewable power, whereas renewable portfolio standards are the opposite, as they are better in two cases in terms of energy saving. The sensitivity analysis indicates that the effect of renewable portfolio standards on the penetration rate of renewable power is constant. There is some potential for the design optimization of these two policies in China, and the characteristics of the power structure should be considered. These results can help the dispatching department in evaluating the impact of carbon emissions trading and renewable portfolio standards to optimize the dispatching strategy and provide directions for policy makers to improve the design.

Multi‐objective economic dispatch of power system with wind farm considering flexible load response

The uncertainty of wind farm power and load both have a certain impact on the economic dispatch of the power system. First, this study deals with the fuzzy processing of power prediction error and load forecasting error of wind farms based on fuzzy random theory. On this basis, a multi-objective fuzzy stochastic dispatch model of a power system with flexible load is established under the carbon trading mechanism. And, the nonlinear cost of flexible load response and the cost of carbon emission compensation is introduced to the multi-objective function of the model. In addition, the fuzzy chance constraint of the spinning reserve is added to the constraint conditions. By introducing the variable, the fuzzy random dispatch model is transformed into a clear equivalent model. Finally, the discrete bacterial colony chemotaxis algorithm is used to process the model, and the optimal solution to the multi-objective function is obtained by a compromise strategy based on a small degree of satisfaction. In the simulation, a classic IEEE10 system and a wind farm are taken as examples. The results show that compared with the other three traditional dispatch models, the total power generation cost is reduced by US$13 670, US$5610 and US$86 010, respectively using the model proposed in this article.

Solving dynamic economic and emission dispatch in power system integrated electric vehicle and wind turbine using multi-objective virus colony search algorithm

Climate change and development of today’s power grids have led to unprecedented changes in the old power grid in terms of fossil fuel consumption level and environmental pollution. So, the ancillary power plants and services have found a good position in power systems. In this paper, renewable energy generators such as wind energy units and plug-in electric vehicles (PEVs) are being integrated in the multi-objective dynamic economic emission dispatch (DEED) problem during a day, which minimizes wind-thermal electrical energy cost and emissions obtained by fossil-fueled power generators at the same time. To obtain a real-world problem, consider several practical and nonlinear constraints such as valve-point effect, ramp-up/ramp-down, prohibited zones, and power generation boundaries. Since the proposed multi-objective problem consists of different variables and constrains, the classical mathematic methods cannot guarantee finding the best solution. Therefore, this paper suggested a new multi-objective virus colony search (MOVCS) based on non-dominated sorting theory and fuzzy decision making for finding the best solution among Pareto fronts. Also, PEVs are remarkable and stochastic power demands and, unlike conventional demands, four dissimilar charging scenarios are considered. The efficiency of the proposed method and model is investigated on some test systems with the presence of wind units and electric vehicles during the day and night. The results are evaluated based on appropriate performance criteria. The results obtained from the proposed algorithm are compared with other algorithms, which indicates the efficiency, accuracy, and speed of this algorithm in solving the proposed problem.

Game Approach to HDR-TS-PV Hybrid Power System Dispatching

Hot dry rock (HDR) power stations have the potential to serve as an energy storage system for large-scale photovoltaic (PV) plants. For flexible operation, thermal storage (TS) power stations are required to coordinate with HDR power stations. In this study, a hybrid power system is constructed by combining the HDR, TS, and PV plants. Game theory is then introduced into the optimal dispatch of the hybrid power system. Considering HDR, TS, and PV as players, non-cooperative and cooperative game dispatching models are established and verified by a case in the Gonghe basin of Qinghai. Finally, the stability of the coalitions and the rationality of allocation of the hybrid power system is verified, and the sensitivity of critical parameters is analyzed. The results demonstrate that the overall payoff of the hybrid power system is increased by 10.15%. The payoff of the HDR power station is increased by 16.5%. The TS power station has obtained 50% of the total extra profits. The PV plant reduces the impact on the grid to obtain the priority of grid connection. Based on these results, a theoretical basis can be provided for developing generation systems based on the HDR resources in the Gonghe Basin.

Distributed optimal resource allocation over strongly connected digraphs: A surplus-based approach

In this brief, a surplus-based approach is proposed to solve the distributed optimal resource allocation problem over directed graphs. Two features distinguish our method from the existing distributed resource allocation schemes. The first one is that the proposed method can be implemented on general strongly connected graph and overcomes the asymmetry caused by the digraphs. The second one is that the proposed method enjoys the privacy-preserving property and avoids the privacy information leakage in the process of information exchange. The distributed algorithm is designed and analyzed by virtue of primal–dual methods, projected gradient, and eigenvalue perturbation theory. Furthermore, distributed economic dispatch in power system, as a simulation example, is used to illustrate the effectiveness of the presented distributed algorithm.

Power System Dispatch With Marginal Degradation Cost of Battery Storage

Battery storage is essential for the future smart grid. The inevitable cell degradation renders the battery lifetime volatile and highly dependent on battery dispatch, and thus incurs opportunity cost. This paper rigorously derives the marginal degradation cost of battery for power system dispatch. The derived optimal marginal degradation cost is time-variant to reflect the time value of money and the functionality fade of battery and takes the form of a constant value divided by a discount factor plus a term related to battery state of health. In case studies, we demonstrate the evolution of the optimal marginal costs of degradation that corresponds to the optimal long-term dispatch outcome. We also show that the optimal marginal cost of degradation depends on the marginal cost of generation in the grid.