High penetration of photovoltaic panels in distribution grid can bring the grid to its operation limits. The main focus of the paper is to determine maximum photovoltaic penetration level in the grid. Three main criteria were investigated for determining maximum penetration level of PV panels; maximum voltage deviation of customers, cables current limits, and transformer nominal value. Voltage deviation of different buses was investigated for different penetration levels. The proposed model was simulated on a Danish distribution grid, considering grid parameters and operating condition in Denmark. Three different PV location scenarios were investigated for this grid: even distribution of PV panels, aggregation of panels at the beginning of each feeder, and aggregation of panels at the end of each feeder. Load modeling is done using Velander formula. Since PV generation is highest in the summer due to irradiation, a summer day was chosen to determine maximum PV penetration for the grid.
Abstract:A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner's quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners' (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.
By taking subsidies out of the picture, wind farm operators (WFO) face new challenges to participate in electricity markets. While conventional producers benefit from dispatchable generation, wind farms with stochastic nature have a challenging job to compete with these players in the market and need to come up with alternative solutions. To this end, energy storage has a great potential in managing the volatile generation and thereby increasing the profit of WFOs. Moreover, the gas market opens new opportunities to improve the flexibilities of WFOs in addressing the incurred penalties due to deviation between prediction and generation. For the sake of practicality, this paper proposes a joint operation-planning model. The WFO bids in both the day-ahead electricity market and gas market while also investing in alternative facilities, including electrical energy storage, gas storage, power-to-gas (P2G), and gas-to-power (G2P). The proposed framework is formulated as a mixed-integer nonlinear programming (MINLP) model. To guarantee to find the global solution, the original MINLP model is recast into a mixed-integer linear programming (MILP) model. Several case studies are defined to capture the potential of the proposed framework on the profit of the WFO, scrutinizing the performance of different facilities and interactions with the aforementioned markets. The modeling provides a tool for the WFOs for considering different alternative approaches to deal with the uncertainty of generation. This includes storing the wind farm surplus generation directly into electrical energy storage or by converting this surplus into gas via P2G and either storing it in gas storage or selling it in the gas market. Moreover, under the lack of generation condition, the electrical energy storage can provide electricity, or the gas from the gas market and gas storage can turn to electricity through G2P to assist WFOs. Results show the effectiveness of the proposed framework in enhancing the profitability of wind farms via different alternatives while highlighting the role of the gas market as a promising solution.INDEX TERMS Electricity market, electrical energy storage, gas storage, gas-to-power, gas market, operation and planning, power-to-gas, wind farm. NOMENCLATUREA. Indexes
This paper proposes an intelligent algorithm for dealing with high penetration of renewable energy sources (RESs) in the medium voltage (MV) by intelligently managing electric vehicles (EVs), as one of the grid flexible loads. The MV grid used in this work is a CIGRE benchmark grid. Different residential and industrial loads are considered in this grid. The connection of medium voltage wind turbines to the grid is investigated. The solar panels in this study are residential panels. Also, EVs are located among the buses with residential demand. The study is done for different winter and summer scenarios, considering typical load profiles in Denmark. Different scenarios have been studied with different penetration level of RESs in the grid. The results show the capability of the proposed algorithm to reduce voltage deviations among the grid buses, as well as to increase the RES penetration in the grid by intelligent management of EVs.
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