Energy Storage System Analysis Review for Optimal Unit Commitment (original) (raw)
Related papers
Storage management by rolling stochastic unit commitment for high renewable energy penetration
Electric Power Systems Research, 2018
This paper presents a unified unit commitment and economic dispatch model that integrates storage devices for the short-term operations scheduling of power systems with high renewable penetration. The presented model is a single multi-hour look-ahead real-time tool that uses multiple time resolution to contain computational requirements. The decisions for the first time interval are binding while the decisions of the remaining scheduling horizon are advisory. Adopting this approach, storage facilities are more efficiently utilized, by constantly adapting their energy injection/withdrawal schedule based on updated system information, thus, alleviating the problem of defining the appropriate stored energy level during economic dispatch. The proposed model is presented in both deterministic and stochastic frameworks. The operational impacts of storage and the benefits of implementing stochastic optimization are validated via extensive simulations using data from the Greek Interconnected Power System.
IET Smart Grid, 2021
Recently, the provision of the reserve from energy storage systems (ESSs) is introduced as a source for ancillary services to address the uncertainties of renewable power generations. The performance of ESSs is analysed while they are applied as a provider of regulation reserves. It has been revealed that previous stochastic models neglect the impact of corrective dispatches, related to the provision of regulation reserves, on the energy level stored in the ESSs, which can lead to large deviations. This study coordinates the stored energy of ESSs to be feasible regarding the dispatches in the base schedule and rescheduling within scenarios. Also, the wind speed fluctuations are considered as the source of uncertainty, and scenarios of wind energy are generated using the Weibull distribution function. The IEEE 24-Bus standard test system is applied for the examination of the proposed model. The results show that the proposed model can manage the performance of ESSs in rescheduling within scenarios, while the coordinated reserve provision of ESSs can remove the concerns about insufficient stored energy of ESSs. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Utilization of hydro pumped-storage (PS) units in conjunction with thermal generating units (TGU) could serve not only as an effective means for providing ancillary services and levelizing demand curve but also as a mechanism for lowering costs for TGUs operation. The objective of this study is to develop a multi-constraint (leveling demand curve, providing spinning reserve, and decreasing fuel and start-up costs (TC)) solution of the unit commitment (UC) problem with TGUs and considerations for PS units (PSUC). After determining the output power of PS units, a new optimization methodology (NOM) is examined to solve the UC problem for TGUs. The NOM benefits from a newly developed fitness function. The application of proposed methodologies is examined for power systems, used in other studies, for a 24 hour operation period. For the UC, the results show improvements between 0.01 and 4.16% in TC, as compared with the best results reported in the literature. The PSUC results show that the savings in TC for this study, when PS units are dispatched for leveling demand curve and providing spinning reserve, are 2.62 and 2.65%, respectively. Also, as a result of utilizing PS units, the demand curve is leveled and all of spinning reserve requirement is met.
Stochastic Unit Commitment Incorporating Demand Side Management and Optimal Storage Capacity
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2018
High penetration of wind energy imposes several operational challenges due to its uncertainty and intermittent nature. Flexible energy resources represent key solutions to compensate for power mismatch associated with wind power (WP) uncertainty and intermittency. This paper proposes a new stochastic unit commitment (SUC) problem formulation including high penetration of wind energy, energy storage system (ESS), and demand side management. Firstly, the Latin hypercube sampling is combined with Cholesky decomposition method to generate different WP scenarios. The simulated scenarios are then reduced using the fast forward selection algorithm. Finally, a novel SUC formulation implements these reduced scenarios to size the ESS optimally, considering its cost and benefit maximization of wind energy. To validate the proposed approach, a nine-unit test system is used to demonstrate the reduction in the operational cost and the increase in the utilized wind energy under different operational conditions.
Stochastic Multi-Fidelity Scheduling of Flexibility Reserve for Energy Storage
IEEE Transactions on Sustainable Energy, 2019
This paper proposes a continuous-time two-stage stochastic optimization model for multi-fidelity co-optimization of energy and flexibility reserve provided by generating units and energy storage (ES) devices in day-ahead operation. The flexibility reserve, defined as a single continuous-time trajectory that combines the balancing and ramping reserves, not only supplies the energy deviation but also the ramping requirements of load and renewable generation in power systems operation. The proposed model co-optimizes decision variables with different modeling fidelity, where the energy and flexibility reserve schedules are modeled and optimized by Bernstein polynomials of different degrees to match the flexibility requirements of load and renewable generation in day-ahead and real-time operation stages. Numerical studies, conducted on the IEEE reliability test system with the load and solar data of California ISO, highlight the benefits of the proposed stochastic multi-fidelity model over traditional discrete-time models in efficient utilization of ES flexibility to supply the energy and ramping requirements of the net-load and avoid scarcity events. Index Terms-Energy storage, multi-fidelity modeling, stochas-tic optimization, continuous-time unit commitment, flexibility reserve, mixed integer linear programming. NOMENCLATURE A. Symbols and Indices k Index of generating units h Index of energy storage (ES) devices ω Index of stochastic process realizations u/d Index of up/down reserve services j Index of time intervals n Index of linearization segments t Continuous time T Scheduling horizon Ω, ˆ Ω Infinite-and finite-dimensional sample space B. Parameters K, H Number of generating units and ES devices D(t), d ω (t) Stochastic load process and its realizations G R (t), g R ω (t) Stochastic renewable generation process and its realizations N (t), n ω (t) Stochastic net-load process and its realizations D 0 (t), N 0 (t) Mean function of load and net-load G R 0 (t) Mean function of renewable generation c d (t, t) Covariance function of load c g (t, t) Covariance function of renewable generation c n (t, t)
Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies
IEEE Access, 2019
This paper addresses the allocation of Energy Storage Systems (ESSs) in power grids by finding the optimal number of ESSs and their locations and sizes with the goal of improving reliability in contingency states. We propose a contingency-sensitivity-based heuristic to decide the optimal number of ESSs and the most effective locations for ESSs support, while circumventing the combinatorial nature of the siting problem. A contingency sensitivity index (CSI) is proposed which represents the impacts of contingencies on the network buses. The CSI ranks the buses, such that those with higher impacts have the privilege for installing ESSs. For the ESSs being fixed, the sizing is formulated as a multi-period AC optimal power flow (OPF) problem and solved by Self-Organizing Hierarchical Particle Swarm Optimization with Time Varying Acceleration Coefficients (HPSO TVAC). The optimal ESSs sizes are selected by minimizing a total cost, which includes investment cost of storage devices, bus voltage deviation cost and average network losses cost. Uncertainties of the renewable generation are accounted by considering different realizations of the generation profiles, then, ESSs sizes are selected by taking the worst case approach. The proposed methodology has been demonstrated on the modified IEEE 30-bus system and Tunisian Grid. The obtained results show the effectiveness of the proposed methodology and the related reliability merits. INDEX TERMS Contingency sensitivity matrix, energy storage systems, multi-period OPF, power grid, siting and sizing.
The Impact of Distributed Energy Storage on Total Operation Cost in Power Systems
2016
High economic operation cost in power systems is one of the challenges toward the development of the new smart distribution networks. Designing an energy storage system (ESS) is an essential part to manage and control the total operation cost as well as improve reliability and security in power systems. One of the major concerns of installing ESS is the transmission lines capacity constraint. Therefore, the right location of ESS is important to maximize the economic benefits. In this paper, centralized and distributed ESS with fixed capital cost are proposed to minimize the total operation cost. Economic dispatch (ED) technique and unit commitment (UC) are formulated to calculate the minimum operation cost. Constraints of thermal units, transmission lines and energy storage are included to formulate the proposed approach. Mixed integer programming (MIP) is used to model the ED and UC as well as the penetration of ESS. A six-bus system is used in all the examples studied to show the ...
Flexible Operation of Batteries in Power System Scheduling With Renewable Energy
IEEE Transactions on Sustainable Energy, 2015
The fast growing expansion of renewable energy increases the complexities in balancing generation and demand in the power system. The energy-shifting and fast-ramping capability of energy storage has led to increasing interests in batteries to facilitate the integration of renewable resources. In this paper, we present a two-step framework to evaluate the potential value of energy storage in power systems with renewable generation. First, we formulate a stochastic unit commitment approach with wind power forecast uncertainty and energy storage. Second, the solution from the stochastic unit commitment is used to derive a flexible schedule for energy storage in economic dispatch where the look-ahead horizon is limited. Analysis is conducted on the IEEE 24-bus system to demonstrate the benefits of battery storage in systems with renewable resources and the effectiveness of the proposed battery operation strategy.
IEEE Access
In this paper, a bi-level optimization model including the problem of transmission network market and energy management in the distribution substation is presented. In the proposed bi-level model, the lower level includes the demand-side management (DSM) program and the optimal charge/discharge of large-scale energy storage system (LSESS) at distribution substations to increase grid profits and send decisions to the upper-level transmission market operator. The upper level of the proposed model is a security-constrained unit commitment (SCUC) to minimize production, no-load, startup, shutdown, and active power curtailment costs, and also the unavailability of the generation units. In this paper, to solve the bi-level optimization problem, the Karush-Kuhn-Tucker (KKT) equation modeling method will be used to turn the problem into a single-level problem. One of the advantages of converting a bi-level model to a single-level model compared to the methods of the decomposition algorithms is the lack of use of iterative algorithms, which leads to an increase in problem-solving time. The proposed model is tested on standard distribution substations and transmission networks, which shows that the proposed method is more effective than decomposition algorithms in terms of problem-solving time. The simulation results showed that the proposed method can be more efficient in large optimization problems.
Journal of Intelligent & Fuzzy Systems, 2018
Inclusion of renewable energy resources with existing conventional generation resources summons revisit to optimization methods used in the field of generation scheduling. The Unit Commitment problem in itself is a highly convoluted problem governed by complex time varying constraints. It gets even more complicated when additional constraints are added due to inclusion of renewable generation backed up by battery storage system. An effort has been made in this paper to improve the model for solving the Unit Commitment problem of conventional thermal generation in conjunction with renewable energy based generation system with storage. A hybrid artificial intelligence based multiple stage solution methodology is envisaged to provide a techno-economical optimal solution to the problem. The proposed methodology provides economically better solution to the Unit Commitment problem of ten thermal generators when integrated with battery supported wind and solar generation. The overall operational cost gets reduced due to integration of renewable resources which gets further reduced by incorporating battery with a novel optimized charge/discharge scheduling technique.