Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability

The hybrid energy storage system (HESS) which consists of battery and ultracapacitor can efficiently reduce the substation energy cost from grid and achieve the peak shaving function, due to its characteristics of high-power density and high-energy density. The sizing of HESS affects the operation cost of whole system. Besides, operation stability (like substation peak power and voltage fluctuations) is rarely considered in urban rail transit (URT) when sizing optimization of HESS is considered. Thus, this research proposes a sizing and control strategy optimization of HESS in URT. First, the mathematic model of URT with HESS is established, which is used to simulate URT and HESS operation state by power flow analysis method. Then, based on the proposed HESS control principle, a bi-level optimization of HESS in URT is proposed. The master level aims to optimize the rated capacity and power of HESS, reducing total operational cost. Then, the HESS control strategy is optimized at slave level, reducing substation peak power and voltage fluctuations of URT. The case study is conducted based on the data of Merseyrail line in Liverpool. A comparison is also conducted, which shows that the proposed method can reduce daily operation cost by 12.68% of the substation, while the grid energy cost is decreased by 57.26%

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Ultracapacitors for port crane applications: Sizing and techno-economic analysis

The use of energy storage with high power density and fast response time at container terminals (CTs) with a power demand of tens of megawatts is one of the most critical factors for peak reduction and economic benefits. Peak shaving can balance the load demand and facilitate the participation of small power units in generation based on renewable energies. Therefore, in this paper, the economic efficiency of peak demand reduction in ship to shore (STS) cranes based on the ultracapacitor (UC) energy storage sizing has been investigated. The results show the UC energy storage significantly reduce the peak demand, increasing the load factor, load leveling, and most importantly, an outstanding reduction in power and energy cost. In fact, the suggested approach is the start point to improve reliability and reduce peak demand energy consumption

Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi‐objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.©2019 IET. This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

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

Assessment of Energy Storage Alternatives in the Puget Sound Energy System

As part of an ongoing study co-funded by the Bonneville Power Administration, under its Technology Innovation Grant Program, and the U.S. Department of Energy, the Pacific Northwest National Laboratory (PNNL) has developed an approach and modeling tool for assessing the net benefits of using energy storage located close to the customer in the distribution grid to manage demand. PNNL in collaboration with PSE and Primus Power has evaluated the net benefits of placing a zinc bromide battery system at two locations in the PSE system (Baker River / Rockport and Bainbridge Island). Energy storage can provide a number of benefits to the utility through the increased flexibility it provides to the grid system. Applications evaluated in the assessment include capacity value, balancing services, arbitrage, distribution deferral and outage mitigation. This report outlines the methodology developed for this study and Phase I results

Expansion planning of power distribution systems considering reliability : a comprehensive review

One of the big concerns when planning the expansion of power distribution systems (PDS) is reliability. This is defined as the ability to continuously meet the load demand of consumers in terms of quantity and quality. In a scenario in which consumers increasingly demand high supply quality, including few interruptions and continuity, it becomes essential to consider reliability indices in models used to plan PDS. The inclusion of reliability in optimization models is a challenge, given the need to estimate failure rates for the network and devices. Such failure rates depend on the specific characteristics of a feeder. In this context, this paper discusses the main reliability indices, followed by a comprehensive survey of the methods and models used to solve the optimal expansion planning of PDS considering reliability criteria. Emphasis is also placed on comparing the main features and contributions of each article, aiming to provide a handy resource for researchers. The comparison includes the decision variables and reliability indices considered in each reviewed article, which can be used as a guide to applying the most suitable method according to the requisites of the system. In addition, each paper is classified according to the optimization method, objective type (single or multiobjective), and the number of stages. Finally, we discuss future research trends concerning the inclusion of reliability in PDS expansion planning