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The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

This study presents a new method for allocation of network losses to distributed generations (DGs) and consumers connected to radial distribution networks in context of liberalised electricity market. The study intends to deal with three aspects, namely, issue of non‐linear relation between power flow and losses, change in network losses because of voltage variation and contribution of DG to the network loss. The proposed method is a branch‐oriented approach based on tracing the real and reactive power of DGs and loads. It establishes a direct relationship between sending and receiving node voltages and apparent power available at the receiving node of a branch. It allocates the losses without any assumptions and approximations by employing backward sweep network reduction technique. The proposed method is simple and effective; and requires only the power flow solution to allocate the losses to DGs and loads. Pro rata method, which is based on the power rating of generators and consumers, is revisited and modified according to branch oriented approach. To show the effectiveness of the proposed method, it has been tested on two test networks, that is, 28‐ and 33‐node test networks. The obtained results are presented, discussed and compared with other published methods. [ABSTRACT FROM AUTHOR]

Under deregulation and competitive environment, equitable allocation of losses with increasing loads and participation of new generations is a challenging task. This study proposes a solution to loss allocation problem for radial distribution network with various load models and distributed generation (DG). The location, rating and time evolution of the loads and DGs connected to the network are considered in this problem. It traces not only real but also reactive power to identify the actual contribution of loads and DGs into the total network losses. It develops a direct relation between injected current/power of consumers/DGs and network losses. To allocate the fair losses, the proposed method assigns the cross‐terms by employing the geometric scheme of allocation. To show the effectiveness of the proposed method, it has been tested on 33‐node radial network. To validate the proposed method, it has been compared with other methods. [ABSTRACT FROM AUTHOR]