Abstract
The access of photovoltaics can reduce the carbon emissions of the integrated energy system and can also improve the economics of terminal energy supply, but the uncertainty of photovoltaic output also brings greater challenges to the optimal operation of the system. This paper focuses on coordinated optimization for the multiple energy systems in consideration of demand response. Latin hypercube sampling and the K-means algorithm are used to generate acceptable scenarios to deal with the photovoltaic uncertainty. Demand response based on Time-of-Use (TOU) electricity price is employed to realize the peak load shifting, and in consequence to improve the system operation. The optimization objective is to minimize the operational cost, subject to the constraints of electric grids, natural gas grids, and hot water pipeline grids. Due to the nonconvex constraints of these grids, the constraints are relaxed by means of the mixed integer linear programming approach, and the whole problem is established as a mixed integer linear programming model. Case studies show that demand response in each energy system and the coordinated optimization between the multiple energy systems can reduce the operational cost of the whole system. Even though the photovoltaic uncertainty results in a higher operational cost, the system has a more reliable operating point.
Highlights
Compared to the conventional energy supply, multiple energy sources and their coordinated operation in the integrated energy systems can improve the efficiency and the economics of the whole system and the terminal consumption [1]
This paper focuses on coordinated scheduling for the integrated energy system with multiple energy hubs in consideration of photovoltaic uncertainty and demand response
Each energy station is connected by the energy pipe network, and the energy can be transmitted among multiple energy stations to realize the efficient use of energy. (46) is the power balance constraint in each energy station, (47) is the heat balance constraint in each energy station, equation (48) is the cold balance constraint in each energy station, and equation (49) is the gas balance constraint in each energy station
Summary
Compared to the conventional energy supply, multiple energy sources and their coordinated operation in the integrated energy systems can improve the efficiency and the economics of the whole system and the terminal consumption [1]. In consideration of different dynamic responses of electric networks, natural gas networks, and heating/cooling networks, different dynamic models are established to integrate the control systems [14]. In consideration of uncertain states of storages in the integrated energy system, [21] investigates a robust model for integrated power, gas and heat grids. The integrated energy systems include multiple kinds of loads, e.g., gas loads and power loads. This paper focuses on coordinated scheduling for the integrated energy system with multiple energy hubs in consideration of photovoltaic uncertainty and demand response. The impacts of different photovoltaic outputs and demand responses on the integrated energy systems with the multiple energy stations are investigated. Single energy station equipment consists of: CCHP unit, gas boiler (GB), multiple energy storage equipment, electric refrigeration (ER), heat pump (HP), roof photovoltaic (PV) power generation device. Where, Pex and Pex are minimum and maximum power purchased and sold by the grid
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