Technological progress of distributed generation (DG) units, global approach to reduce the pollution emissions, and creating the opportunity for local investors to participate in the generation expansion investment, are some of the important reasons to involve the DG as an alternative option in the GEP. In this regard, expansion of the DG units compared to the expansion of centralized generation units should be evaluated economically and technically. Therefore, this paper proposes a novel approach for modeling the composite centralized and distributed generation expansion planning problem in the regulated power system. An integrated objective function is presented to involve the most important decision-making factors overall the power system. The purpose is to find the optimal combination of generation expansion created by power plants and DG units to minimize the total costs related to generation expansion, operation, maintenance, fuel, emission, distribution loss and expected energy not supplied, for the decision-making period. The proposed model reveals as a mixed integer nonlinear programming where, genetic algorithm is applied to find the optimal solution which contains the capacity, technology and operation strategy of non-stochastic DG units that will be optimally allocated into some distribution feeders. Results confirm the effectiveness and superiority of the proposed approach.

分布式发电（DG）单位的技术进步，减少污染排放的全球方法以及为本地投资者提供参与发电扩展投资的机会，是将DG纳入GEP替代方案的一些重要原因。在这方面，应从经济和技术上评估与集中式发电机组相比，DG机组的扩展。因此，本文提出了一种新的方法来对调节式电力系统中的集中式和分布式发电扩展计划进行建模。提出了一个综合目标函数，涉及整个电力系统中最重要的决策因素。目的是找到由发电厂和DG单位创建的发电扩展的最佳组合，以最大程度地减少决策期间与发电扩展，运行，维护，燃料，排放，配电损耗和未提供的预期能源相关的总成本。该模型以混合整数非线性规划为例，其中采用遗传算法找到了最优解，该最优解包含了非随机DG机组的容量，技术和运行策略，这些最优分配将最优地分配给某些配电馈线。结果证实了该方法的有效性和优越性。该模型以混合整数非线性规划为例，其中采用遗传算法找到了最优解，该最优解包含了非随机DG机组的容量，技术和运行策略，这些最优分配将最优地分配给某些配电馈线。结果证实了该方法的有效性和优越性。该模型以混合整数非线性规划为例，其中采用遗传算法找到了最优解，该最优解包含了非随机DG机组的容量，技术和运行策略，这些最优分配将最优地分配给某些配电馈线。结果证实了该方法的有效性和优越性。