Microgrid working conditions identification based on cluster analysis – a case study from Lambda Microgrid

This article presents the application of cluster analysis (CA) to data proceeding from a testbed microgrid located at Sapienza University of Rome. The microgrid consists of photovoltaic (PV), battery storage system (BESS), emergency generator set, and different types of load with a real-time energy management system based on supervisory control and data acquisition. The investigation is based on the area-related approach - the CA algorithm considers the input database consisting of data from all measurement points simultaneously. Under the investigation, different distance measures (Euclidean, Chebyshev, or Manhattan), as well as an approach to the optimal number of cluster selections. Based on the investigation, the four different clusters that represent working conditions were obtained using methods to define an optimal number of clusters. Cluster 1 represented time with high PV production; cluster 2 represented time with relatively low PV production and when BESS was charged; cluster 3 represents time with relatively high PV production and when BESS was charged; cluster 4 represents time without PV production. Additionally, after the clustering process, a deep analysis was performed in relation to the working condition of the microgrid

Operation and Planning of Energy Hubs Under Uncertainty - a Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs

Operation and planning of energy hubs under uncertainty - A review of mathematical optimization approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.Web of Science117228720

Optimal placement of switched capacitors equipped with stand-alone voltage control systems in radial distribution networks

This study presents a method for optimally selecting the location and size of switched shunt capacitors equipped with stand-alone voltage control systems and fixed shunt capacitors in radial distribution networks. The main contribution of this paper is the introduction of a novel algorithm specifically designed for placement of capacitors equipped with stand-alone voltage control systems. The considered objective function is the net saving calculated for every potential solution by using a backward-forward load flow technique. A genetic algorithm is used to maximize the objective function while taking into account the technical constraints of the distribution network and the maximum capacitors' sizes depending on the set points of the stand-alone voltage control system and on the locations of capacitors. The effectiveness of the proposed optimization method is verified and compared with other prevalent methods by simulation results carried on 69-bus and 28-bus distribution networks with three load levels

A novel strategy for optimal placement of locally controlled voltage regulators in traditional distribution systems

In this paper, an approach for placement of voltage regulators (VRs) in traditional distribution systems by considering a local controller model is presented. The main aims of this paper are controlling the voltage level in its permitted range and decreasing the costs imposed to the distribution system companies, such as costs that stem from power losses, VRsâ\u80\u99 investment and maintenance. Genetic algorithm (GA) has been used as a tool to determine the number, location and rated power of VRs. Since in traditional distribution systems, tap position determination of VRs is achieved by local controllers, local controller model is established to determine tap operations. A 70-bus distribution system is considered to prove the value of the presented approach. Effectiveness of the proposed approach and ineffectiveness and infeasibility of conventional approaches are presented in numerical studies. The presented approach allowed to eliminate voltage violation in all load conditions and a reduction of power losses of about 6% for the maximum load level

Coordination of Neighboring Active Distribution Networks under Electricity Price Uncertainty Using Distributed Robust Bi-Level Programming

Distributed energy resources transform the passive distribution networks into active distribution networks (ADNs). Coordinating the dispatch actions of distributed resources has been studies in the literature, both within an ADN and between an ADN and the transmission system. However, the direct coordination between ADNs interconnected via a physical tie line is a topic boldly under-discussed, despite its practical relevance. In this paper, we consider the problem of coordinating the dispatch actions, including the energy exchange, of two interconnected ADNs, each one integrated with flexible loads (managed by demand response aggregators), energy storage systems, and microturbines (MTs). The bilateral energy trading enables the neighboring ADNs to benefit from the difference in locational marginal prices. The coordination problem is formulated as a robust bi-level program under price uncertainty. At the upper level, the total cost of the ADNs is minimized subject to the uncertainty of electricity market prices and technical constraints of the networks and the resources. At the lower level, the DR aggregators present at each ADN selfishly minimize their own cost. Moreover, the worst case realization of wholesale electricity market prices is considered. The problem is linearized using the Karush-Kuhn-Tucker (KKT) conditions and decomposed using the alternating direction method of multipliers (ADMM). Simulation results verify the convergence behavior of the proposed method and quantify the value of DSO-DSO coordination in the presence of an interconnecting line between the ADNs.</p