Overview of current development in electrical energy storage technologies and the application potential in power system operation

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

A summary of demand response in electricity markets

Demand side management: Benefits and challenges ☆

Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail new challenges in power systems and more volatility in power prices in liberalized electricity markets. Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed generation. The economic implications of grid-scale electrical energy storage technologies are however obscure for the experts, power grid operators, regulators, and power producers. A meticulous techno-economic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a holistic cost analysis framework. To this end, this study critically examines the existing literature in the analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing Monte Carlo method to consider uncertainties. The examined energy storage technologies include pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries (e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D support services, and frequency regulation.

Electrical energy storage systems: A comparative life cycle cost analysis

Energy storage for mitigating the variability of renewable electricity sources: An updated review

This paper introduces the cumulant method for the probabilistic optimal power flow (P-OPF) problem. By noting that the inverse of the Hessian used in the logarithmic barrier interior point can be used as a linear mapping, cumulants can be computed for unknown system variables. Results using the proposed cumulant method are compared against results from Monte Carlo simulations (MCSs) based on a small test system. The Numerical Results section is broken into two sections: The first uses Gaussian distributions to model system loading levels, and cumulant method results are compared against four MCSs. Three of the MCSs use 1500 samples, while the fourth uses 20 000 samples. The second section models the loads with a Gamma distribution. Results from the proposed technique are compared against a 1000-point MCS. The cumulant method agrees very closely with the MCS results when the mean value for variables is considered. In addition, the proposed method has significantly reduced computational expense while maintaining accuracy.

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Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions

This paper presents a risk-based approach for evaluating the participation strategy of a battery storage system in multiple markets. Simultaneous offering in day-ahead energy, spinning reserve, and regulation markets is considered in this paper. The uncertainties considered include predicted market prices as well as energy deployment in spinning reserve and regulation markets. A new nonprobabilistic model is introduced in this paper to handle the uncertain nature of spinning reserve and regulation markets. Robust optimization is implemented to model these uncertain parameters and manage their related risk. The proposed risk-based model is a max–min problem, which is converted to its equivalent ordinary maximization problem using duality theory. The presented model is linearized by implementing strong duality theory. Finally, the proposed method is tested and verified using an illustrative case study.

Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets

In this paper, a Latin supercube sampling (LSS) combined with Monte Carlo simulation is presented to efficiently sample random variables in the probabilistic power flow (PPF) problem. The results of the LSS method are compared with other techniques, namely Latin hypercube sampling (LHS) and simple random sampling (SRS), using bin-by-bin histogram comparison. The simulation results are presented for the case of IEEE 118-bus test system.

Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling

In this paper, new optimal power flow (OPF) techniques are proposed based on multiobjective methodologies to optimize active and reactive power dispatch while maximizing voltage security in power systems. The use of interior point methods together with goal programming and linearly combined objective functions as the basic optimization techniques are explained in detail. The effects of minimizing operating costs, minimizing reactive power generation, and/or maximizing loading margins are then compared in both a 57-bus system and a 118-bus system, which are based on IEEE test systems and modeled using standard power flow models. The results obtained using the proposed mixed OPFs are compared and analyzed to suggest possible ways of costing voltage security in power systems.

William Rosehart

Papers

Energy storage for mitigating the variability of renewable electricity sources: An updated review

Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions

Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets

Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling

Multiobjective optimal power flows to evaluate voltage security costs in power networks