The main focus of this paper is to find an upgrade plan for a microgrid. This plan includes system lines’ upgrades, the place, and size of the new capacitors and DERs. As usual, the planning process is begun with the load forecasting. The main objectives include minimization of system upgrade cost, loss cost, loss cost in peak load, and finally demand cost. The last objective is realized here using the tap changer of the main station transformer and reactive power support of the sources available or planned for the future of the grid. This is always referred to as the conservation of voltage reduction. In order to consider the effects of bus voltages on the system demand, the ZIP model for the system loads is used. Particle swarm optimization is used to minimize the total cost. The line flow limits lower/upper bound on bus voltages and minimum/maximum producible active and reactive power from different sources are considered as the optimization constraints. The proposed algorithm is tested on the IEEE 69 bus test system and the results are discussed.

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. IEEE 30-bus radial distribution test system is used as an illustrative example to show the effectiveness of the proposed method.

The success of the energy transition in Europe depends on the engagement of citizens and the sustainable replacement of conventional generation with renewable production. Highlights of the PAN European Technology Energy Research Approach (PANTERA) project, a H2020 coordination and support action, are presented in this paper. In broad terms, PANTERA offers a forum for actors in the smart grid to support the expansion of activities in smart grid research, demonstration, and innovation, especially in the below-average spending member states in the European Union (EU). The focus of this paper is on those activities of the project consortium related to the identification of gaps and barriers in regulations, standards, and network codes that hinder the sustainable engagement of energy citizens in the energy transition. The paper summarises the challenges to citizen engagement in the energy transit and considers the enablers that make the engagement of citizens viable, e.g., demand response (DR), renewable energy resources (RESs), and modern designs for local energy markets (LEMs). We focus on the barriers to the enablers that are explicitly and implicitly related to regulations, standards, and network codes and explore aspects of the relevant regulations and standards of the sample below-average spending member states. A specific case study of a research and demonstration project in Ireland for updating the network codes is also presented to demonstrate the ways in which member states are attempting to remove the barriers and enable citizen participation in the smart energy transition. Finally, the opportunities to foster smart grid research and innovation through shared knowledge and insights offered by the PANTERA European Interconnection for Research Innovation and Entrepreneurship (EIRIE) platform are highlighted.

The status of switches in distribution systems might be changed for protection purposes or to achieve the operation objectives. However, the system topology should be known for the efficacious management and control of these systems. In most practical systems, due to the lack of enough measurements, the topology cannot be identified accurately and speedily. The forecast output can be transformed into the joint probability density function (pdf) of uncertain parameters, e.g., power demands. The measured data and this pdf are first conflated to update the joint pdf to best comply with the measurements. The topology identification problem, i.e., finding the probability of each topology given a set of measured values, is converted to a simpler form, i.e., finding the probability of observing these measured values in this topology. These probabilities are then calculated by tracing how the measurements affect the shape of the new consolidated pdf in each topology compared to the original pdf. The proposed technique employs as much data as available, is able to find the accurate probabilities, and is yet quite fast. The mathematical treatment of extracting such probabilities is presented and the proposed technique is validated in numerical studies in terms of accuracy, speed, and versatility.

The frequent actuation of discrete control devices (DCDs), e.g., on-load tap changers, drastically reduces their lifetime. This, in turn, imposes a huge replacement cost. Simultaneous scheduling of these DCDs and continuous control devices, e.g., distributed energy resources, is imperative for reducing the operating cost. This also increases the lifetime of DCDs and helps to avoid the sub-optimal/infeasible solutions. Considering the high cost of discrete control actions (DCAs), they may never be justified against the other options in a short scheduling horizon (SH). With a longer SH, their benefits over a long period justify DCAs. However, a shorter SH helps to hedge against the risk impelled by uncertainties. Here, the system future is modeled as a set of multi-period scenarios. The operator exploits a long SH, but solely applies the decisions made for the first period and waits for updated data to make the next decisions. This enables cost reduction by strategically applying DCAs prior to the time that they are inevitable, while avoiding them when unneeded. The proposed branch-and-cut-based solution methodology accurately deals with DCAs while applying some expediting heuristics. During the branching process, a globally convergent trust region algorithm solves the integer relaxed problems.

An indispensable step towards coordinating the actions of distribution and transmission system operators (DSOs-TSO) is to estimate the range of flexibility that can be offered to the TSO by DSOs. Within this context, a data-driven probabilistic approach is proposed to evaluate the capability of a distribution network in providing active and reactive power support in real-time. To this end, in an offline phase, a linear discriminant analysis model, together with a piecewise linear model of the distribution network are trained to delineate the boundary of a region representing the adherence to distribution network operational constraints. In the implementation phase, this region comprises the feasible set of a series of optimization problems, formulated to determine the support provision capability. These optimization problems are of iterative linear programming type, which allows for real-time applicability. The evaluated support capability can be deemed as the available reserve in real-time transmission operation, which enables providing a coordinated response towards unexpected events, and facilitates the participation of distributed resources in the balancing market by granting an up-to-date estimation of available supports. This approach is tested on the IEEE 123-node system and verified through comparison with an AC optimal power flow technique.