Abstract
Energy storage is an important element of an energy system. In the power system, energy storage can be defined as a component that can be employed to generate a form of energy or utilize previously stored energy at different locations or times when it is required. Energy storage can enhance the stability of the grid, increase the reliability and efficiency of integrated systems that include renewable energy resources, and can also reduce emissions. A diverse set of storage technologies are currently utilized for the energy storage systems (ESSs) in a varied set of projects. This chapter provides information about the current ESS projects around the world and emphasizes the leading countries that are developing the applications of ESSs. The main categories of ESSs are explained in this chapter as follows: electrochemical, electromechanical, electromagnetic, and thermal storage. Moreover, the energy storage technologies are utilized in power grids for various reasons such as electricity supply capacity, electric energy time-shifting, on-site power, electric supply reserve capacity, frequency regulation, voltage support, and electricity bill management. Additionally, by integrating the various energy forms and developing the concept of multi-energy systems, ESSs become a fundamental component for the efficient operation of multi-energy systems. The main role of ESSs in multi-energy systems is to compensate for the fluctuations in power output from renewable energy resources. Moreover, the performance of the multi-energy system increases when it got integrated with an ESS. In this chapter, the applied ESS technologies in the context of the multi-energy systems are presented and explained.

Abstract
This article provides a framework for coordinating the operation of multiple microgrids with hydrogen systems in a distribution network considering the uncertainties of wind and solar power generation as well as load demands. The model is based upon a bilevel stochastic programming problem. On the upper level, the distribution system is the leader with a profit-maximization goal, and the microgrids are followers with cost-minimization goals on the lower level. The problem is solved by transforming the model to a single-level model using Karush-Kuhn-Tucker (KKT) conditions and linearized using McCormick's relaxation and Fortuny-Amat techniques. Unlike previous studies, both levels are modeled as scenario-based stochastic problems. Moreover, the scenarios associated with uncertain variables are obtained from a real data set. After preparing the data set, scenarios are reduced using a machine learning-based clustering approach. An application of the coordinated operation model is developed for a distribution network containing several microgrids. By solving the problem, the optimal amount of power exchange and the clearing price between microgrids and distribution systems are determined. Moreover, the proposed bilevel model made 13% more profit for the distribution system than the centralized model. Also, the effects of integrating hydrogen systems with microgrids on increasing the flexibility of operators are investigated.

Abstract
This paper proposes a two-phase approach for optimal short-term operational scheduling with intermittent renewable energy resources (RES) in an active distribution system. The first phase determines the amounts of purchased power from the market and the unit status of distributed generation (DG) and feeds the data into the second phase, a real-time scheduling coordination with hourly network reconfiguration. The two-phase proposed approach is applied to a case study of a sixteen-bus test system that uses synthetic data from renewable power generators and forecasts local user demands with a sampling time of five minutes.

Abstract
This paper discusses static transmission expansion planning (STEP) in terms of minimizing the costs of investment and operations. We propose a transmission expansion model that divides into investment and operations problems. We use a binary particle swarm optimization algorithm (BPSO) to solve the investment problem and a DC optimal power flow (DCOPF) to solve the operations problem. We model uncertainty as stochastic demand at each node. A simulated case study numerically evaluates the efficiency of the proposed method. © 2013 IEEE.

Abstract
This study presents a multi-objective framework to evaluate the integration of distant wind farms with associated transmission network upgrades on optimal power system planning. The presented approach also extends the technique to include the consideration of energy limitations associated with the installed hydro generation facilities. This study attempts to emphasise on the reliability implications rather than the production cost evaluation aspects. The decision making is based on hierarchal level II (HL-II) Expected Energy Not Served as an entire power system reliability assurance, and capital cost plus annual operational cost as an economical index. Non-dominated Sorting Genetic Algorithm is adopted to achieve the Pareto front of the aforementioned multi-objective problem. A fuzzy satisfying method, designated as the distance metric, is used to represents a trade-off between different objectives. To numerically evaluate the efficiency of the proposed method, simulation results on three case studies are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed method. © The Institution of Engineering and Technology 2013.

Abstract
This paper attempts to propose a fair solution in generation scheduling problem in the presence of inherent uncertainties in short-term power system operation. The proposed methodology incorporates probabilistic methodology in the uncertainties representation section, while harmony search algorithm is adopted as a fast and reliable soft computing algorithm to solve the proposed nonlinear, non-convex, large-scaled and combinatorial problem. As an indispensable step towards a more economical power system operation, the optimal generation scheduling strategy in the presence of mixed hydro-thermal generation mix, deemed to be the most techno-economically efficient scheme, comes to the play and is profoundly taken under concentration in this study. This paper devises a comprehensive hybrid optimisation approach by which all the crucial aspects of great influence in the generation scheduling process can be accounted for. Two-point estimation method is also adopted probabilistically approaching the involved uncertain criteria. In the light of the proposed methodology being implemented on an adopted test system, the anticipated efficiency of the proposed method is well verified. © 2014 Taylor & Francis.