Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment

Abstract

In deregulated power systems Distribution Network Operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components and by using some optimization methods for reducing the active losses. The event of Distributed Generation (DG) has introduced new challenges to these distribution networks both at the planning and operation stages. The role of Distributed Generation (DG) units must be correctly assessed to optimize the overall operating and investment cost for the whole system. However the Distributed Generation Owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This paper presents a long-term dynamic multi-objective model for planning of distribution networks regarding the benefits of DNO and DGOs. The proposed model simultaneously optimizes two objectives, namely the benefits of DNO and DGO and determines the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on distributed generation units and network reinforcements over the planning period. The proposed model also considers the uncertainty of electric load, electricity price and wind turbine power generation using the point estimate method. The effect of benefit sharing is investigated for steering * Corresponding author

Electric Power Grids Distribution Generation System For Optimal Location and Sizing – An Case Study Investigation by Various Optimization Algorithms

“…Constraints (8) and (9) represent the balance equations and reactive power, respectively. Restrictions (10), (11) and (12) represent the equations of active power flow, reactive and apparent power, respectively. The restrictions (13), (14) and (15), consider the power limits injected by the DG, limits voltage network and load flow limits, respectively.…”

“…The authors showed that the combination of GA with simulated annealing was more effective than using only GA. In [11] a method to maximize the benefit to network operators and owners of distributed generation in a deregulated electricity market hybrid algorithm was presented. As well as simultaneously optimizing the benefits to the distribution company and to the owner of the DG their method also considered the uncertainty of demand and energy prices.…”

Risk-Averse Preventive Voltage Control of AC/DC Power Systems Including Wind Power Generation

“…Using the technique described in [36], the satisfaction level of each objective function is calculated and described in Table V. It is observed from this table that the best compromise solution in this strategy is Sol 23 .…”

A Study of the Factors Influencing the Optimal Size and Site of Distributed Generations

“…GA is search algorithm based on the mechanics of natural selection and natural genetics of living beings [10], [11]. It is a popular meta-heuristic, evolutionary optimization based algorithm.…”