Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a...
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Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a voltage regulation method for ADNs to minimize the operational losses while keeping the nodal voltages within the limit with the reduced PV power curtailment and the reduced switching numbers of on-load tap changers (OLTCs) and capacitor banks (CBs). Meanwhile, the proposed voltage regulation method also aims to minimize the reactive power flowing through OLTCs, and to minimize the switching numbers of substation CBs. In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. The MINP model is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical decision-making algorithm is developed to select the best solution from the Pareto optimal set. Moreover, the decentralized voltage regulation aims at mitigating real-time nodal voltage variations via adjusting the real-time active and reactive power of each PV plant. Several simulations and comparisons are carried out on a modified IEEE 33-node system to verify the effectiveness of the proposed methods, and to compare with some previous voltage regulation methods. Simulation results show that the proposed voltage regulation methods can not only effectively control voltage variations of ADNs but also improve the economics of ADNs, substations, and PV plants.
Renewable energy source integration in combined cooling, heating, and power (RES-CCHP) systems can improve energy efficiency and foster renewable energy consumption, which is a promising solution to the current energy...
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Renewable energy source integration in combined cooling, heating, and power (RES-CCHP) systems can improve energy efficiency and foster renewable energy consumption, which is a promising solution to the current energy and environmental crisis. This paper studies the off-design characteristics of these systems, establishes a two-way interaction mechanism between the system capacity and operation strategy, and proposes a two-stage nested optimization design method for the RES-CCHP. In the first stage, the capacity of each system component is obtained by the genetic algorithm and then used as the constraint for the operation optimization. In the second stage, the nonlinearprogramming algorithm is employed to optimize the operational energy consumption, costs, and CO2 emissions taking the off-design characteristics of core devices into consideration. Several case studies are conducted to verify the feasibility of the two-stage optimal design method. It was found that the two-stage nested optimization design increases the primary energy saving ratio, cost saving ratio, and carbon dioxide emission reduction ratio by 4.5%, 4.32%, and 3.27% compared with the conventional optimal design method, respectively. Overall, the proposed two-stage nested optimization design was tested to improve the performance and the renewable energy consumption.
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