Day-ahead Economic Dispatch Research Articles

A Multi-Timescale Coordinated Optimization Framework for Economic Dispatch of Micro-Energy Grid Considering Prediction Error

The integration of a large number of renewable energy sources (RES) into the micro-energy grid (MEG) brings certain challenges to its optimal dispatching, especially the impact of RES and load prediction error on optimal dispatching. Therefore, this paper proposes a multi-timescale coordinated optimization framework for economic dispatch of MEG considering prediction error, including three stages: day-ahead economic dispatch, intra-day rolling optimization and real-time adjustment. In the day-ahead and intra-day stages, the device's output is solved with the objective of optimal system economy. The MEG is divided into three subsystems in the real-time adjustment stage using distributed model predictive control (DMPC). The subsystems cooperate to correct the intra-day device output, which reduces the complexity of solving system optimization problems. Meanwhile, considering that the prediction horizon in DMPC usually remains constant during the optimization, which affects the control performance of DMPC. This paper analyzes the impact of the prediction horizon and control horizon on the control performance of DMPC. Then a DMPC method with adaptive adjustment of the prediction horizon according to the prediction errors of RES and loads power is proposed, which can balance the stability and prediction accuracy of the system. Finally, the case studies verify the feasibility of the proposed strategy.

Day-ahead economic dispatch of building energy system considering virtual energy storage

For the increasing proportion of building energy consumption in global energy consumption in recent years, this paper proposes a day-ahead economic dispatching method of building energy systems considering virtual energy storage. Firstly, virtual energy storage is modeled according to the thermal inertia of the building. Then, a building energy system model with a distribution grid, photovoltaic generator sets, and heating equipment (heat pump) is constructed. Finally, a day-ahead economic optimal dispatch of the building energy system considering virtual energy storage is modeled to achieve the charging and discharging management of virtual energy storage. Case study results show that under the precondition for meeting the thermal comfort of the occupants, the daily electric power purchase of the building energy system after considering virtual energy storage is reduced by 278.69 kW. The daily comprehensive operating cost is decreased by 8.3%. The validity and reliability of this suggested approach in this paper are proved.

Chance-Constrained Generic Energy Storage Operations Under Decision-Dependent Uncertainty

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.

Non-iterative multi-area coordinated energy and reserve allocation model for hybrid AC/DC power systems with flexible frequency operation

As the result of renewable energy natural intermittency, load shedding and renewables curtailment are inevitable to keep supply and demand balance in high penetration level power systems. Luckily, with accurate estimation and proper allocation of the reserve, power fluctuation can be smoothed appreciably. Therefore, this study presents a novel coordinated day-ahead economic dispatch model for both energy and reserve in bulk hybrid AC/DC power systems. Flexible frequency operation for each regional sub AC system is applied to estimate reserve demand, while renewable energy forecast error is described by the versatile probability distribution. Considering practical operation structure in China, a non-iterative bi-layer dispatch algorithm is proposed to share power and reserve between interconnected subsystems by applying DC tie-lines’ flexibility. This bi-layer structure is achieved through the upper layer deciding the boundary variables with aggregated generation information provided by the lower layer. Then each lower layer regional AC grid solves its own programming with the boundary variables. Simulation is carried out to verify the efficacy of the proposed dispatch model and distributed solution methodology.

Corrigendum: Day-ahead economic dispatch strategy for distribution network considering total cost price-based demand response

Corrigendum: Day-ahead economic dispatch strategy for distribution network considering total cost price-based demand response

Low-Carbon Economic Dispatch of Integrated Electricity-Gas Energy System Considering Carbon Capture, Utilization and Storage

With the rapid development of modern industry, while improving people’s living standards, the over-exploitation of coal, oil and natural gas has led to a shortage of fossil energy, global warming and an increasingly serious deterioration of the ecological environment. To mitigate the greenhouse effect caused by excessive carbon emissions, the vigorous development of integrated electricity-gas system (IEGS) dominated by clean energy is the future trend of sustainable development of energy systems. In this paper, a bi-level optimal scheduling model is proposed for an IEGS considering carbon capture, utilization and storage (CCUS), and the ladder carbon trading mechanism is introduced to convert carbon emissions into economic benefits. The upper model is an optimal distribution model of natural gas network, and the lower model is a day-ahead economic dispatch model of power system. Based on the Karush-Kuhn-Tucher (KKT) condition and strong duality theory of the lower model, the bi-level model is transformed into a mixed integer linear programming (MILP), which is solved by calling CPLEX through the Yalmip toolbox of the Matlab platform. Finally, the reasonableness and validity of the model are verified by three arithmetic simulations. The results show that the proposed bi-level model for low-carbon economic dispatch of IEGS considering CCUS can effectively reduce the operating costs and carbon emissions of the system.

Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources

Under the background of energy internet and low-carbon power, integrated energy system (IES) has become an important carrier of energy conservation and emission reduction. The IES utilizes innovative energy management mode to coordinate various energy sources such as natural gas, electric energy and heat energy. It is composed of energy production, conversion, storage and consumption subsystems, which emphasizes breaking the isolation of energy subsystems through reasonable scheduling, realizing energy cascade utilization and improving energy utilization efficiency. In this paper, an energy management model with two-stage scheduling before day and in real time is proposed aiming at the collaborative optimization of generator-load-storage of IES. Firstly, the first stage is the day-ahead economic dispatch, which aims to realize the power distribution of units in the system. The day-ahead economic dispatching model takes the maximization of economic benefits, the maximization of exergy efficiency and the minimization of carbon emission cost as the optimization objectives, so as to make the day-ahead global optimal dispatching decision. Secondly, the second stage is real-time optimal scheduling, which aims at real-time power adjustment of the previous scheduling plan. The real-time optimal scheduling model takes the minimum interactive power deviation punishment cost, wind abandonment punishment cost and user satisfaction loss cost as optimization objectives, so as to balance the energy supply and load demand deviation between planned output and actual output. Thirdly, according to the characteristics of the model, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the first-stage day-ahead economic scheduling, and YALMIP toolbox is used to solve the real-time optimal scheduling model. Finally, based on the established model, a typical IES is selected for case simulation, which verifies that the proposed method can effectively improve the economy of system operation.

Marginal utility of battery energy storage capacity for power system economic dispatch

The battery energy storage (BES) is recognized as a key resource for the power fluctuations smoothing, peak load shaving and frequency regulation, and its performance depends heavily on the available capacity. It is meaningful to investigate the influence of the BES capacity on the power system operation cost. This paper focuses on the marginal utility of BES capacity in the day-ahead power system operation cost. Firstly, the day-ahead economic dispatch problem is formulated as a constrained quadratic programming problem, with the consideration of the BES degradation cost. Then, the relationship between the BES-related shadow prices and the marginal electricity price is derived from the KKT conditions of the dispatch problem. Based on it, the marginal utility of BES capacity is modeled to evaluate the effect of BES capacity on the system day-ahead operation cost. Finally, the numerical results demonstrate that the proposed marginal utility can manifest the influence of BSS capacity on the power system operation.

Three-stage Coordinated Dispatching of Wind Power-Photovoltaic-CSP-Thermal Power Combined Generation System

Aiming at the problem that the grid connection of intermittent renewable energy such as wind power and photovoltaics brings great uncertainty to the power system, a three-stage coordinated scheduling strategy of wind power-photovoltaic-concentrated solar energy is proposed. CSP Combined heat and power system. In the first stage, based on the day-ahead forecast data of wind power, photovoltaics and load, a “peak shaving” model of the combination of wind power-photovoltaic-CSP was established. In the second stage, according to the value of the remaining load after optimization in the first stage, a day-ahead economic dispatch model for thermal power units is established. In the third stage, based on the start-stop status and output of thermal power units in the first and second stages, the output of CSP plants and the intraday forecast data of wind power, photovoltaics, and loads, the establishment of an intraday thermal power joint regulation model and the establishment unit and CSP stations are established, and intraday scheduling plans are arranged. Case analysis shows that the proposed CHP system scheduling strategy can be supplemented by thermal power and CSP during load peak and trough periods, thereby minimizing the uncertainty brought by grid integration of wind power and photovoltaics. Introduce.

Price-based response strategy of micro-energy-grid in energy-anciliary service markets

Under the reform of China’s power market, micro-energy-grid further enhances its own profitability by participating in the joint dispatch of energy trading and frequency regulation ancillary services. Aiming at the operation characteristics of multi-energy complementary, this paper proposes a price-based response strategy of micro-energy grid with different scenarios in the joint dispatch considering both energy trading and frequency regulation ancillary services. The dispatch structure and process of the micro-energy-grid are firstly proposed, following with the day-ahead economic dispatch strategy, intra-day optimization rolling strategy and real-time frequency regulation mileage allocation strategy. Finally, the cases study shows that the multi-energy response strategy closely follows the price fluctuations of power spot market, with the deviation energy and total energy cost respectively reducing by 17.34% and 19.61% through energy trading and frequency regulation, which can further reduce the total cost of energy consumption and make significance for the dispatch of micro-energy-grid.

Corrigendum: Day-Ahead Economic Dispatch of AC/DC Hybrid Distribution Network Based on Cell-Distributed Management Mode

Wei Wei, Tonglin Hao, Tao XuKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China* Correspondence: taoxu2011@tju.edu.cnKeywords: AC/DC hybrid distribution network, cell-distributed management mode, alternating direction method of multipliers, data-driven distributionally robust optimization, day-ahead economic dispatchCorrigendum on: Wei W, Hao T and Xu T (2022) Day-Ahead Economic Dispatch of AC/DC Hybrid Distribution Network Based on Cell-Distributed Management Mode. Front. Energy Res. 10:832243. doi: 10.3389/fenrg.2022.832243Incorrect Funder NameThere is an error in the Funding statement. The correct Name for the Funder is **National Key Research and Development Program of China**. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.The correct funding:This paper is supported by the National Key Research and Development Program of China( 2020YFB2104500): Research and application demonstration project of the urban integrated energy system with intelligent IoT control technology.

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

To alleviate the issues of global warming and energy crisis, countries are vigorously developing renewable energy technology. The integration of large-scale renewable energy, including wind energy, hydropower, and photovoltaic (PV), has a great impact on system operation scheduling and economic dispatch. This paper presents an economic dispatching method of wind-PV-CSP-hydro-battery system with wind and photovoltaic power generation as the main energy sources. The long short-term memory (LSTM) neural network is applied to predict wind and PV power, besides, the Latin Hypercube Sampling (LHS) method and the synchronous reduction algorithm are used to obtain 10 typical wind and PV power scenarios. A day-ahead economic dispatch model of wind-PV-CSP-hydro-battery mathematical model is established, and relevant constraints are considered. Concentrated solar power (CSP), hydropower stations, batteries, and transferable loads are used as flexible resources to increase the penetration rate of wind and photovoltaic power generation. Finally, three cases are tested to demonstrate the feasibility of the proposed model. The results show that: (1) LSTM neural network can well predict the output power of wind and photovoltaic power generation with a small root mean square error (RMSE). (2) The introduction of transferable loads and CSP power station into the renewable energy power system can effectively reduce the fluctuation and curtailment rates of wind power and PV power generation.

Day-Ahead Economic Dispatch Strategy for Distribution Network Considering Total Cost Price-Based Demand Response

The guidance of EV via a price-based demand response is of great significance to the security and economy of the distribution network. However, the current price-based demand response mechanisms fail to consider the spatial-temporal distribution of large-scale EVs connected to the distribution network. For tackling this challenge, this paper proposes a day-ahead economic dispatch strategy for distribution networks considering total cost price-based demand response. A two-layer model of the day-ahead economic dispatch for the distribution network is utilized to obtain the interactive calculation framework for the total cost price. The total cost price iterates between the economic dispatch model of the distribution network and the total cost price-based demand response model until no significant changes in total cost price are observed. Among them, the price-responsive load considers spatial-temporal shift of the EV charging load. Finally, simulation study case based on a modified IEEE 33-bus system is employed to demonstrate the effectiveness of this strategy for the economic operation of the distribution network.

Day-Ahead Economic Dispatch of AC/DC Hybrid Distribution Network Based on Cell-Distributed Management Mode

Current development trends suggest that future networks will be AC/DC hybrid distribution networks (AC/DC HDNs) operating under the Cell-distributed management mode (CDMM). Cross-Cell transaction in the economic dispatch of CDMM-based distribution networks is necessary to ensure Cell demand, improve the level of renewable energy generation outpower consumption, and enhance the economic efficiency of the whole system. To realize adjacent Cell and cross-Cell transactions in this management mode, this paper proposes a day-ahead economic dispatch model of AC/DC HDNs based on CDMM. The proposed model includes a power transaction stage and a security check stage. In the power transaction stage, a Cell-to-Cell transaction model based on the alternating direction method of multipliers is established. This model forms a consistent transaction plan between different Cells on the basis of information exchange between the Cell operator and the Cell system operator. In the security check phase, a security check model based on data-driven distributionally robust optimization is developed to reduce the transfer power according to various security considerations. Alternate iteration of these two stages enables the day-ahead economic dispatch of AC/DC HDNs based on CDMM for adjacent Cell and cross-Cell transactions. Finally, the effectiveness of the proposed model is verified through a numerical example. The differences among four dispatch models are analyzed to illustrate the need for cross-Cell transactions, and the impact of the confidence level on the dispatch results is discussed.

A Day-Ahead Economic Dispatch Scheme for Transmission System With High Penetration of Renewable Energy

The great uncertainty caused by the high penetration of renewable energy brings severe challenges to the economic dispatch of transmission systems. In this study, a day-ahead economic dispatch model is proposed. The model is based on the coordination framework of transmission and distribution, and it fully considers the response potential of the distribution network. The scenario generation method based on the Copula function is used to describe the uncertainty of the energy output of renewable energy sources such as wind power, and then, the response potential of the distribution network is analyzed. A day-ahead economic scheduling framework for transmission and distribution coordination is proposed, which is solved by a deep reinforcement learning algorithm. Finally, the effectiveness of the proposed method is verified through application to the IEEE 6-bus transmission network and the IEEE 7-bus distribution network.

Multi-Stage Distributionally Robust Stochastic Dual Dynamic Programming to Multi-Period Economic Dispatch With Virtual Energy Storage

A virtual energy storage (VES) model is proposed in this paper to accommodate renewable energy under a special market regulation. Such VESs can provide or consume electricity to the main power grid under the premise that the daily net electricity energy is balanced. Furthermore, a multi-stage distributionally robust optimization (MSDRO) model is set up in this paper to address the temporal uncertainties in the day-ahead economic dispatch model. Compared with the traditional two-stage distributionally robust optimization, the proposed multi-stage approach provides more flexibilities so that the decision variables can be adjusted at each time period, leading to a complex nested formulation. To efficiently solve the MSDRO model, a stochastic dual dynamic programming method is employed to decompose the original large-scale optimization model into several sub-problems in the stages, as two steps: forward pass and backward pass. In the forward pass, the expected cost-to-go function is approximated by piecewise-linear functions and then several samples are used to generate a lower bound; the backward pass will generate Benders’ cuts at each stage from the solution of the forward pass. The forward and backward passes are performed iteratively until the convergence is reached. Numerical results on an IEEE 118-bus system and a practical power system in China verify the proposed method.

Low-carbon Operation of Combined Heat and Power Integrated Plants Based on Solar-assisted Carbon Capture

Accelerating the development of renewable energy and reducing CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions have become a general consensus and concerted action of all countries in the world. The electric power industry, especially thermal power industry, is the main source for fossil energy consumption and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. Since solvent-based post-combustion carbon capture technology would bring massive extra energy consumption, the application of solar-assisted carbon capture technology has attracted extensive attention. Due to the important role of coal-fired combined heat and power plants for serving residential and industrial heating districts, in this paper, the low-carbon operation benefits of combined heat and power integrated plants based on solar-assisted carbon capture (CHPIP-SACC) are fully evaluated in heat and power integrated energy system with a high proportion of wind power. Based on the selected integration scheme, a linear operation model of CHPIP-SACC is developed considering energy flow characteristics and thermal coupling interaction of its internal modules. From the perspective of system-level operation optimization, the day-ahead economic dispatch problem based on a mix-integer linear programming model is presented to evaluate the low-carbon benefits of CHPIP-SACC during annual operation simulation. The numerical simulations on a modified IEEE 39-bus system demonstrate the effectiveness of CHPIP-SACC for reducing CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions as well as increasing the downward flexibility. The impact of different solar field areas and unit prices of coal on the low-carbon operation benefits of CHPIP-SACC is studied in the section of sensitivity analysis.

Data-driven adjustable robust Day-ahead economic dispatch strategy considering uncertainties of wind power generation and electric vehicles

Considering the intermittent features of wind power generation and electric vehicles, it is important for microgrid to formulate available dispatch strategy while ensuring the system economy. A two-stage data-driven adjustable robust optimization (ARO) model is presented in this paper to realize an optimal day-ahead economic dispatch strategy for the microgrid considering the uncertainties of wind power generation and electric vehicles. In the optimization model, an imprecise-Dirichlet-model-based nonparametric ambiguity set is constructed without any presumption about the probability distributions of the uncertainties. Further, a polyhedron uncertainty set obtained from the ambiguity set is driven by historical data, and this becomes narrower with an increase in the amount of the historical data used to construct the ambiguity set. The data-driven ARO problem is converted into a traditional two-stage ARO model with the obtained polyhedron uncertainty set. Further, this can be decomposed into a mixed integer linear programming (MILP) master problem and a sub problem with a max–min structure. Then, duality theory, Big-M method, and column and constraint generation (C&CG) algorithm are applied to achieve the optimized day-ahead economic dispatch strategy for the microgrid. Case studies illustrate the solution robustness, economic benefit, flexible adjustment capability and uncertainty depiction ability of the presented method.

A hierarchical dispatch method for tri-level integrated thermal and power systems

Owing to the increase in the installed capacity of combined heat and power (CHP) units, electric power systems (EPS) and district heating systems (DHS) couple more strongly by CHP plants at the physical level to produce EPS–CHP plant–DHS tri-level integrated thermal and power systems (ITPS). However, various ITPS components are traditionally operated by different enterprises in an uncooperative mode, which results in non-optimal operational conditions and a serious wind curtailment problem. In this study, a generalized hierarchical dispatch method based on the day-ahead economic dispatch problem for a tri-level ITPS is developed to coordinate its different constituents. It decomposes the ITPS economic dispatch problem into an EPS sub-problem, a DHS sub-problem, and a CHP plant sub-problem. The lower level systems transfer so-called feasible region of boundary variables (FRBV) and optimal function of boundary variables (OFBV) to the upper level systems. After that, the upper level systems compute the optimal values of the boundary variables based on the FRBV and OFBV and transmit them to the lower level systems. The nonlinear CHP plant sub-problem is linearized first and then solved utilizing a grid-based method. The results of case studies show that the relative errors of applying the linearization approximation to the nonlinear terms in the CHP plant sub-problem do not exceed 3%. Furthermore, while the solution obtained by the hierarchical dispatch method is less optimal than the ideal situation, the deviations of the optimal values computed for different stages of the heating seasons do not exceed 1.5%. Finally, the simulation data produced for a real urban tri-level ITPS indicate that the hierarchical dispatch method is very efficient for practical application.

Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems

To comprehensively consider the impact of uncertainties of variable energy resources (VERs) and load demands on the scheduling plans, the paper proposes a multi-time scale coordinated optimization (MTSCO) method of the energy hub (EH) with multi-energy flows and multi-type energy storage systems (MESSs). The MTSCO involves with global optimization of day-ahead economic dispatch (DAED), local intra-day receding-horizon optimization (IDRHO) and real-time adjustment (RTA). DAED formulates the 24-period output of controllable devices integrated in EH through minimizing its operation cost of a single day. Following the formulated DAED plan, IDRHO minimizes the sum of energy purchase cost and MESSs’ penalty cost within every 15 min. Further, RTA provides real-time feedback to IDRHO and minimizes the total adjustment amount of all controllable devices every 5 min. The simulation results indicate that the proposed multi-time scale dispatching and MESSs enable to promote the EH economy, acquire better dispatching solution aiming at uncertainties of VERs and varying load demands and alleviate the grid’s compensation for fluctuation.