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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]

In the context of a deregulated electricity market, this paper presents a new method for the distribution of electrical losses in distribution systems with local generators, incorporating variations in consumer power factor. Additionally, the paper examines how local generators may gain the utmost possible advantage from their contributions to system performance enhancement. The proposed approach initially analyzes dynamic changes in active and reactive power flow caused by a change in consumer power factor, followed by a loss allocation mechanism. This method is developed based on Kirchhoff’s law and the natural decomposition of injected bundled power flow at a bus. To completely eliminate cross-subsidies, the proposed mechanism follows a two-stage power flow: first, consumer-controlled power flows, and second, determining variations in power flow as compared to the first stage due to interactions with local generators. Consumer load modeling is being considered to improve the proposed method’s adaptability and efficiency across different distribution systems. The effectiveness of the proposed method is validated through testing on 30-bus and 69-bus distribution systems, and the results obtained are compared with those of other methods found in the literature. [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]

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]