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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 paper proposes a new method for loss allocation in radial distribution networks (DNs) considering different models of distributed generation (DG) and load in context of a deregulated environment. In the proposed method, a direct relation between real/reactive power flow in a branch and its losses has been developed without taking any assumption and approximation. Suitable expressions/relations for network power flow have been developed employing power summation algorithm. The developed expressions do not contain any cross-terms. For allocating the losses among network participants, the proposed method uses a circuit based branch oriented approach. Using only power flow results, this method employs a backward sweep network reduction technique to allocate the network losses to load/DG at various nodes. This method does not require additional step of normalization to collect the exact amount of total network losses. In the present study, different types of DG, e.g. DG injecting only real power, DG injecting only reactive power, DG injecting real power and absorbing reactive power, and DG injecting both real and reactive power are considered to allocate losses. In addition to this, various load models based on impact of voltage variation on real/reactive power consumption are also considered. To test the proposed method, modified 9-node and 33-node radial DNs have been considered. In order to show the effectiveness of the proposed method, its numerical results have been compared with those by other methods available in the literature. [ABSTRACT FROM AUTHOR]