Distribution energy storage investment prioritization with a real coded multi-objective genetic algorithm

Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm - II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure

Relieving Tensions on Battery Energy Sources Utilization among TSO, DSO, and Service Providers with Multi-Objective Optimization

The European strategic long-term vision underlined the importance of a smarter and flexible system for achieving net-zero greenhouse gas emissions by 2050. Distributed energy resources (DERs) could provide the required flexibility products. Distribution system operators (DSOs) cooperating with TSO (transmission system operators) are committed to procuring these flexibility products through market-based procedures. Among all DERs, battery energy storage systems (BESS) are a promising technology since they can be potentially exploited for a broad range of purposes. However, since their cost is still high, their size and location should be optimized with a view of maximizing the revenues for their owners. Intending to provide an instrument for the assessment of flexibility products to be shared between DSO and TSO to ensure a safe and secure operation of the system, the paper proposes a planning methodology based on the non-dominated sorting genetic algorithm-II (NSGA-II). Contrasting objectives, as the maximization of the BESS owners’ revenue and the minimization of the DSO risk inherent in the use of the innovative solutions, can be considered by identifying trade-off solutions. The proposed model is validated by applying the methodology to a real Italian medium voltage (MV) distribution network

Special Issue: Control, Optimization and Planning of Power Distribution Systems

The use of renewable energy sources is moving the generation from the top to the bottom of power systems, where traditionally only loads existed [...

Multi-objective Optimization of Energy Storage System in an Italian Local Energy Community

Energy Storage Systems are becoming necessary for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. Since their costs are still high, optimal planning and management of these devices are crucial to identify specific configurations that can support storage installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal storage systems location and their optimal schedule. In the paper, a novel Multi-Objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm – II integrated with a real codification that allows joining in a single optimization all the main features of an optimal storage implementation project. The paper is focused on the potential of a Local Energy Community of residential prosumers (with photovoltaic and storage systems) that can support the operation of the distribution system. In particular, pilots selected in the EU project StoRES (Promotion of higher penetration of distributed PV through storage for all) constitutes the Local Energy Community. Application examples are presented to illustrate the algorithm effectiveness

A Robust Approach to manage Demand Response for power distribution system planning

The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network

Planning for “High Quality” Distribution Networks

Voltage dips are the most common power quality disturbances. They can often cause serious damages to the end- users, especially to the industrial ones or those involved in services that today are almost entirely based on digital processes. Thus, their mitigation is assuming a key role for both customers and distributors. The first ones look for eliminating any obstacle to the correct operation of their processes, and are often willing to stipulate premium contracts that guarantee a high level of quality to their power supply. The second ones have to solve power quality problems in their networks with restrict available budget, first of all, to achieve the Regulatory targets on quality but also to satisfy the most exigent customers. In previous works, the authors proposed a distribution network planning methodology that adds to the traditional technical constraints also the target of the power quality. In terms of voltage dips, the power quality target to be achieved may be limiting the voltage dips frequency in each node of a given network within a prefixed threshold. In this paper, an improved algorithm able to find the most economical solutions to comply with this constraint has been developed. This improved algorithm considers by the same standards different corrective actions, with the aim at finding the best compromise between investment and benefits. Accordingly with this view, the goal is minimizing the cost that the distributors have to sustain for the protective measures, the cost paid by the customers for the damages caused by the not mitigated disturbances and for the premium quality contracts. The effectiveness of the proposed approach is proved by examples in a test network and in a small one derived from real cases

Multi-MicroGrids for innovative distribution networks in rural areas

MicroGrids (MGs) are one of the most promising developments for the distribution system to increase the reliability with respect to disturbances of power delivery and the resiliency of some localised areas with reference to major external events capable to cause extended black-outs. With a distribution design that allows the decomposition into small, autonomous portions like MGs, customers that are not directly involved in the fault can be supplied. In this case, the distribution system acts as a backbone that provides connectivity to several MGs. The system can be regarded as a Multi-MicroGrid (MMG). It requires rethinking the operational/planning methodologies of distribution systems to exploit the opportunities from such a novel arrangement. In this paper, a planning methodology is presented that aims at defining expansion plans for MV distribution systems with MGs, using probabilistic approaches to taking into account the variability of loads and Distributed Generation. It allows the distribution planner to find the optimal development plan of a given distribution network, particularly in rural areas. The application to realistic case studies highlights the benefits from MMG intentional islanding, with reference to the postponement of network investments and improvement of Quality of Service

Robust Distribution State Estimation for Active Networks

A heuristic optimization algorithm based on the Dynamic Programming theory is proposed to find the optimal placement of measurement devices, i.e. to determine their number and position. The optimization procedure explicitly considers network reconfigurations (caused by random faults or by the active management of the network), so that the final measurement system allows the distribution state estimation to provide an accurate estimate of the system status in all the possible practical conditions. The branch currents are taken as state variables for improving the quality of the solution of the state estimator that exploits field measurements and load pseudo-measurements. The uncertainties introduced by the measurement chain are simulated with a Monte Carlo algorithm. Variations of both load demand and network parameters are also modeled in the Monte Carlo algorithm. The provided examples show the effectiveness of the optimization process