Short-term planning is a decision-making process that aims at ensuring proper performance of electrical distribution systems (EDSs) within a short period of time. In recent years, this process has faced a significant challenge due to the integration of renewable energy-based technologies. To handle such complicated planning problem most suitably, sophisticated algorithms are required. This paper proposes a mixed-integer linear programming model to find the optimal short-term plan of EDSs considering siting and sizing of capacitor banks and renewable energy sources, conductor replacement of overloaded circuits, and voltage regulators allocation. Besides considering the economic aspects, the environmental issues are also considered to promote a low carbon emission system. To address the uncertainties of electricity consumption and renewable energy output power, a two-stage robust optimization model is used, and to handle this model more efficiently, the column and constraint generation algorithm is applied. A 135-node distribution system is studied under different conditions to assess the performance of the proposed approach. Results show that the planning actions, for each case study, improve the efficiency of EDS and mitigate the pollutant emissions at the distribution level.

This paper proposes a trilateral bilevel stochastic mixed integer bilevel linear programming (MIBLP) model to handle a joint master-slave operation-planning problem. This model considers the interactions among an integrated community energy system (ICES), prosumers, and the wholesale electricity market (WM). The ICES, in the upper level, makes a joint optimal photovoltaic (PV) and energy storage system plan taking into account the concurrent interactions with the WM and prosumers to maximize its profit. On the other hand, in the lower level, the prosumers aim at minimizing their bills via an optimal PV-package sizing while interacting with the ICES and the WM simultaneously. The strong duality theorem is used to recast this MIBLP model into an equivalent single-level model. Since the lower level is also an operation-planning problem, this recast results in a mixed integer nonlinear programming (MINLP) model that prevents finding a satisfactory solution. To remedy this issue, sensitivity analysis and a separation-based linearization technique are applied. Numerical results illustrate the effective performance of the proposed business model while providing valuable information for the ICES and consumers.

There is an increasing concern about controlling and reducing carbon emissions in power systems. In this regard, researchers have focused on managing emissions on the generation side, which is the main source of emissions. Considering emission limits on the generation side results in an increase in locational marginal prices that negatively affects social welfare. However, carbon emissions are a by-product of electricity generation that is used to satisfy the demands on the consumer side. Consequently, demand side emission control may not be achieved if only generation is taken into account. In order to fill this existing gap, in this paper, a demand-side management approach aiming at carbon footprint control is proposed. First, the carbon footprint is allocated among the consumers using an improved proportional sharing theorem method. Each consumer learns about their real-time carbon footprint, excess carbon footprint, and the incurred surcharge tax. Then, demands are adjusted via a proper adjustment procedure. This provides enough information for consumers where demand management may result in carbon footprint and demand reductions as well as the exemption of incurred taxes. Profit analysis for both generation and demand sides is carried out to show the effectiveness of the proposed framework using two illustrative case studies. The results obtained, compared with existing policies such as carbon cap, cap-and-trade, and carbon tax, prove the fairness and the advantages of the proposed model for both the demand and the generation sides.