Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply system(RMESS)considering virtual energy storage(VES).First,to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes,this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint.Subsequently,an RMESS model encompassing these two types of VES was formulated.This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations.Based on the above models,a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation.Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

Optimal dispatching method of traffic incident rescue resource for freeway network

An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of route distance as the weight to reflect the impact of traffic conditions on the decisions of rescue resources.According to the characteristics of different types of rescue vehicles the dispatching decision-making time is revised to show the heterogeneity among different rescue vehicle dispatching modes. The genetic algorithm is used to obtain the solutions to the rescue resources dispatching model. A case study shows that the proposed method can accurately reveal the impact of potential incidents on the costs of rescues according to the variations in the types and quantities of rescue resources and the optimal dispatching plan with respect to potential incidents can be obtained.The proposed method is applicable in real world scenarios.

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

Optimal Dispatching of Large-scale Water Supply System

This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model, which may be solved by utilizing the hierarchical optimization method, is established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software tool has been developed successfully. The application of this model to the city of Shenyang (China) is compared to experiential strategy. The results of this study show that the developed model is a very promising optimization method to control the large-scale water supply systems.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

State Transition Modeling Method for Optimal Dispatching for Integrated Energy System Based on Cyber-Physical System

The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the IES.This method uses the physical,integration,and optimization layers as a three-layer modeling framework.The physical layer is used to describe the physical models of energy units in the IES.In the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are given.The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state.Numerical simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of different stakeholders.A pilot test was conducted for a typical household.According to the monitored appliance-level data,operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods.Moreover,a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data.Occupant behavior of appliance usage,such as daily turn-on times,turn-on moment,duration of each operation,preference of temperature setting,and flexibility window,were determined by the developed data-mining approach.Based on the established flexible load models and the identified occupant energy usage behavior,a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs,occupants’dissatisfaction,CO_(2) emissions,and the average ramping index of household power profiles.The model was solved with the assistance of the NSGA-III and TOPSIS methods.Results indicate that the proposed framework can effectively optimize the operation of household flexible loads.Compared with the benchmark,the daily electricity costs,CO_(2) emissions,and average ramping index of household power profiles of the optimal plan were reduced by 7.3%,6.5%,and 14.4%,respectively,under the TOU tariff,while those were decreased by 9.5%,8.8%,and 23.8%,respectively,under the dynamic price tariff.The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

System Modeling and Optimal Dispatching of Multi-energy Microgrid with Energy Storage

The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system model based on unified energy flows is proposed to describe the static relationship,and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then,the optimal dispatching model of an MEMG is established as a mixed-integer linear programming(MILP)problem using piecewise linear approximation and convex relaxation.Finally,the system model and optimal dispatching method are validated in an MEMG,including district electricity,natural gas and heat supply,and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.

Optimal flexibility dispatch of demand side resources with high penetration of renewables:a Stackelberg game method

To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.

Multi-objective Optimal Dispatch for Integrated Energy Systems Based on a Device Value Tag

Due to the variety of devices and operating scenarios in an integrated energy system(IES),the optimal dispatch of an IES is usually complicated.An optimal dispatch method for an IES is proposed by defining the scheduling value for each device which can be different under various scenarios.First,thinking over the private and public attributes of each operating equipment,the evaluation system is established with the actual scenarios of economic,environmental and energy-savings being considered.Secondly,the economic,environmental and energy-saving benefits of each operating equipment are quantified by Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS).Therefore,the scheduling value of the device is comprehensively assessed according to the specific scenario.Finally,decomposing the output of the device into direct available energy and indirect available energy,an optimal model is built with the maximum general production benefits as the objective,and is solved by MATLAB and CPLEX.The simulation results show that the evaluation system can reflect multiple values of devices.The proposed model can unify the modeling of optimal dispatch for different scenarios in the IES and can improve dispatch efficiency,while ensuring the accuracy of the results with high computation efficiency.

Day-ahead Optimal Dispatch Model for Coupled System Considering Ladder-type Ramping Rate and Flexible Spinning Reserve of Thermal Power Units

In northern China,thermal power units(TPUs)are important in improving the penetration level of renewable energy.In such areas,the potentials of coordinated dispatch of renewable energy sources(RESs)and TPUs can be better realized,if RESs and TPUs connected to the power grid at the same point of common coupling(PCC)are dispatched as a coupled system.Firstly,the definition of the coupled system is introduced,followed by an analysis on its characteristics.Secondly,based on the operation characteristics of deep peak regulation(DPR)of TPUs in the coupled system,the constraint of the ladder-type ramping rate applicable for day-ahead dispatch is proposed,and the corresponding flexible spinning reserve constraint is further established.Then,considering these constraints and peak regulation ancillary services,a day-ahead optimal dispatch model of the coupled system is established.Finally,the operational characteristics and advantages of the coupled system are analyzed in several case studies based on a real-world power grid in Liaoning province,China.The numerical results show that the coupled system can further improve the economic benefits of RESs and TPUs under the existing policies.

Trends of optimal dispatching of microgrid for fishery based on model predictive control

As a constrained optimization strategy,model predictive control(MPC)is widely used in the optimal dispatch of microgrids.Rolling optimization and feedback correction strategy can effectively deal with the impact due to the system uncertainty and model mismatch which ensures the stable operation of the microgrid.Due to the uncertainty of distributed power sources,MPC can be applied to solve the problem of renewable energy output in microgrid for fishery.This paper reviews the optimal dispatch of microgrid for fishery based on model predictive control.Firstly,this article introduces the basic principles and classification of model predictive control,and then explains the remaining problems and research direc-tions.The characteristics of the microgrid optimal dispatching problem are described,and the analysis are focused on how MPC solves the uncertain problems in the microgrid optimal dispatching.Finally,perspectives and the future research direction of model pre-dictive control in microgrid energy dispatch is reviewed.

Market-oriented Optimal Dispatching Strategy for a Wind Farm with a Multiple Stage Hybrid Energy Storage System

With the increased promotion of integrated energy power systems(IEPS),renewable energy and energy storage systems(ESS)play a more important role.However,the fluctuation and intermittent nature of wind not only results in substantial reliability and stability defects,but it also weakens the competitiveness of wind generation in the electric power market.Meanwhile,the way to further enhance the system reliability effectively improving market profits of wind farms is one of the most important aspects of Wind-ESS joint operational design.In this paper,a market-oriented optimized dispatching strategy for a wind farm with a multiple stage hybrid ESS is proposed.The first stage ESS is designed to improve the profits of wind generation through day-ahead market operations,the real-time marketbased second stage ESS is focused on day-ahead forecasting error elimination and wind power fluctuation smoothing,while the backup stage ESS is associated with them to provide the ancillary service.An interval forecasting method is adopted to help to ensure reliable forecast results of day-ahead wind power,electricity prices and loads.With this hybrid ESS design,supply reliability and market profits are simultaneously achieved for wind farms.

Solution of Combined Heat and Power Economic Dispatch Problem Using Direct Optimization Algorithm

This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.

Study on Risk Dispatch Model of Hydropower Station in the Market Environment

In the power market environment, due to the uncertainty of the reservoir inflow and the pool purchase price, it is very important to research power generation risk dispatch of hydropower plants, taking into consideration the benefits and risk control of both sides. This paper investigates power generation risk dispatch of hydropower plants in the market environment, and proposes a mathematical model which considers maximization of benefits and risk control, reflects control willingness of risk and benefits, resolves it with the PSO algorithm, finding more economic and reasonable results. The feasibility and validity of the model and resolving methods are verified by an example.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.