Day-ahead interval optimization for CCHP system considering uncertainty of wind power and PV

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

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“…Gas turbine operating costs include operating costs, start-up and shutdown costs and environmental costs [29]. They can be defined as follows:…”

Section: Objective Functionmentioning

confidence: 99%

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Yang

Zhang

et al. 2023

Energies

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“…This article refers to the typical daily cooling, heating, and electricity load demand curves in ref and the 24 h time-of-use electricity price curve in ref , used to construct the basic environmental data and related parameters of the system. The wind power generation prediction curve and 24 h DNI prediction curve in this article are generated from a large number of scenarios using Latin hypercube sampling and then obtained by considering the synchronous backpropagation of Kantorovich distance to eliminate the scenarios.…”

Section: Example Analysismentioning

confidence: 99%

Research on CCHP Design and Optimal Scheduling Based on Concentrating Solar Power, Compressed Air Energy Storage, and Absorption Refrigeration

Yang,

Zheng,

Chang

2023

ACS Omega

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In response to the country's "carbon neutrality, peak carbon dioxide emissions" task, this paper constructs an integrated energy system based on clean energy. The system consists of three subsystems: concentrating solar power (CSP), compressed air energy storage (CAES), and absorption refrigeration (AR). Among them, thermal energy storage equipment in the photothermal power generation system can alleviate the fluctuation of solar energy and provide a stable power supply for the system. The compression heat generated during the compression process of the CAES system can be recovered through heat transfer oil to provide a heat load. The compressed air in the air accumulator (ACC) expands in the air turbine to provide an electric load. The low-temperature exhaust gas discharged from the turbine can provide cool load. First, a multienergy system that includes CSP, CAES, and AR is built. Then, the system takes the lowest economic cost as the objective function and constructs the system day-ahead scheduling model. Finally, for data obtained from scene reduction, the commercial optimization software Gurobi is invoked through YALMIP to solve the model. The results show that the three subsystems achieve multienergy complementarity; system operating costs are reduced by 59.94% and fully absorb wind and solar energies by the system.

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“…Electrical power generation from PV array and WT farm is highly uncertain and is affected by solar irradiance and wind speeds [41][42][43][44]. Accordingly, the accurate modeling of the generators of the PV panels and the wind turbine is essential.…”

Section: Modeling Of Renewable Energy Uncertaintymentioning

confidence: 99%

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Shaheen

Ullah

Qais³

et al. 2022

Energies

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Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

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Day-ahead interval optimization for CCHP system considering uncertainty of wind power and PV

Cited by 43 publications

References 27 publications

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Research on CCHP Design and Optimal Scheduling Based on Concentrating Solar Power, Compressed Air Energy Storage, and Absorption Refrigeration

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

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