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Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits. [ABSTRACT FROM AUTHOR]

Battery energy storage is becoming an important part of modern power systems. As such, its operation model needs to be integrated in the state-of-the-art market clearing, system operation, and investment models. However, models that commonly represent operation of a large-scale battery energy storage are inaccurate. A major issue is that they ignore the dependence of the charging power on the battery state of energy. Consequently, market players might suffer great monetary losses for not being able to follow their day-ahead schedule and/or deliver the scheduled reserves. In order to bridge the gap between very detailed low-level battery charging constraints and high-level battery operation models used in the literature, this paper examines a dependence of battery charging ability on its state of energy. It proposes a laboratory procedure, which can be used for any battery type and technology, to obtain this dependence. It also formulates an accurate linear battery charging model, which closely approximates the real-life battery charging constraints. The proposed battery charging model is compared against the models commonly used in the literature. Battery operation schedules obtained by all the models are compared against experimentally obtained results in order to assess the value of the proposed model in real life. [ABSTRACT FROM AUTHOR]

Distributed transmission-scale energy storage is becoming economically feasible due to the growing share of renewable generation and cost reduction of specific storage technologies, primarily batteries. Under these circumstances, independent merchants may start investing in storage facilities. On the other hand, system operators, besides investing in transmission lines, may, under certain conditions, invest in storage units as well. This paper formulates a trilevel model where the upper-level problem optimizes the system operator's transmission line and energy storage investments, the middle-level problem determines merchant energy storage investment decisions, while the lower level problem simulates market clearing process for representative days. After replacing the lower level problem with its primal dual equivalent conditions, the middle- and lower level problems are merged into a mixed-integer problem with equilibrium constraints. The resulting bilevel structure is iteratively solved using a cutting plane algorithm. The proposed formulation is first applied to a six-bus system to present the mechanics of the model and then to the IEEE RTS-96 test system. The results show that even at the low cost of energy storage, the system operator (SO) still prefers line investments, while merchant investments are driven by the volatility of LMPs. Both the SO and merchant investments increase the social welfare, although this increase is mostly driven by the SO investments. [ABSTRACT FROM AUTHOR]

Massive integration of renewable energy resources calls for new operating and planning paradigms, which address reduced controllability and increased uncertainty on the generation side. On the other hand, emerging energy storage technologies can provide additional flexibility. Therefore, generation and storage expansion models need to be coordinated to ensure sufficiency of system-wide response capabilities within different regulation intervals. This paper proposes a coordinated generation and storage expansion formulation considering primary frequency response constraints. This is a stochastic mixed-integer linear program solved using an off-the-shelf solver. The proposed formulation is compared to the case when primary frequency response is neglected. The case study performed for an eight-zone ISO New England test system quantifies the value of energy storage simultaneously providing primary frequency response and spatio-temporal arbitrage. [ABSTRACT FROM AUTHOR]

Suitably located energy storage systems are able to collect significant revenue through spatiotemporal arbitrage in congested transmission networks. However, transmission capacity expansion can significantly reduce or eliminate this source of revenue. Investment decisions by merchant storage operators must, therefore, account for the consequences of potential investments in transmission capacity by central planners. This paper presents a tri-level model to co-optimize merchant electrochemical storage siting and sizing with centralized transmission expansion planning. The upper level takes the merchant storage owner's perspective and aims to maximize the lifetime profits of the storage, while ensuring a given rate of return on investments. The middle level optimizes centralized decisions about transmission expansion. The lower level simulates market clearing. The proposed model is recast as a bi-level equivalent, which is solved using the column-and-constraint generation technique. A case study based on a 240-bus, 448-line testbed of the Western Electricity Coordinating Council interconnection demonstrates the usefulness of the proposed tri-level model. [ABSTRACT FROM PUBLISHER]

Innovations in information, communication, forecasting, and control technologies, along with the high investments to enhance the capabilities of active neighborhoods to participate in crucial grid balancing services like fast frequency control (FFC), have gained wide attention in the research community recently. This paper aims to provide a structured and comprehensive study of the challenges, opportunities, and potentials of active neighborhood resources, e.g., distributed energy resources (DERs) and smart flexible loads, to actively participate in FFC services. Technical details, regulatory requirements, and control strategies with practices in several countries are discussed in detail. Furthermore, the challenges and opportunities for the participation of active neighborhood resources are elaborated and categorized as technical, economic, and other challenges. Finally, a model on coordinated synthetic inertia control (SIC) is demonstrated to examine the DERs like energy storage systems, small scale wind turbine generator, photovoltaic generator diesel engine generator, electric vehicle capability to provide the fast frequency response (FFR). It is observed that proposed SIC framework is potentially capable to arrest high rate of change of frequency (RoCoF) and reduce frequency nadir at different loading conditions. The observations and discussions made in this study would be demystifying for every stakeholder of the energy community. • A comprehensive study of the potentials of active neighborhood resources is presented. • Technical, regulatory, and control practices in several countries are discussed. • Challenges and opportunities are elaborated as technical, economic, and other challenges. • A model is demonstrated to examine the DERs capability to provide the FFR. [ABSTRACT FROM AUTHOR]

Energy storage (ES) is a pivotal technology for dealing with the challenges caused by the integration of renewable energy sources. It is expected that a decrease in the capital cost of storage will eventually spur the deployment of large amounts of ES. These devices will provide transmission services, such as spatiotemporal energy arbitrage, i.e., storing surplus energy from intermittent renewable sources for later use by loads while reducing the congestion in the transmission network. This paper proposes a bilevel program that determines the optimal location and size of storage devices to perform this spatiotemporal energy arbitrage. This method aims to simultaneously reduce the system-wide operating cost and the cost of investments in ES while ensuring that merchant storage devices collect sufficient profits to fully recover their investment cost. The usefulness of the proposed method is illustrated using a representative case study of the ISO New England system with a prospective wind generation portfolio. [ABSTRACT FROM AUTHOR]

Renewable electricity generation not only provides affordable and emission-free electricity but also introduces additional complexity in the day-ahead planning procedure. To address the stochastic nature of renewable generation, system operators must schedule enough controllable generation to have the flexibility required to compensate unavoidable real-time mismatches between the production and consumption of electricity. This flexibility must be scheduled ahead of real-time and comes at a cost, which should be minimized without compromising the operational reliability of the system. Energy storage facilities, such as pumped hydro energy storage (PHES), can respond quickly to mismatches between demand and generation. Hydraulic constraints on the operation of PHES must be taken into account in the day-ahead scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but requires more computational resources than conventional deterministic optimization. This paper proposes a deterministic and an interval unit commitment formulation for the co-optimization of controllable generation and PHES, including a representation of the hydraulic constraints of the PHES. The proposed unit commitment (UC) models are tested against a stochastic UC formulation on a model of the Belgian power system to compare the resulting operational cost, reliability, and computational requirements. The cost-effective regulating capabilities offered by the PHES yield significant operational cost reductions in both models, while the increase in calculation times is limited. [ABSTRACT FROM PUBLISHER]

We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage planning procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96. [ABSTRACT FROM AUTHOR]

For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service. [ABSTRACT FROM PUBLISHER]

The integrated energy system is considered to be an important way to avoid energy supply risks by virtue of advantages in meeting diversified energy demand and improving energy utilization efficiency. Energy storage enables microgrid operators to respond to variability or loss of generation sources. In view of the difficulty of battery to fully improve the energy utilization efficiency and solve the problems of clean energy power large-scale consumption, and the difficulty of single demand response to significantly improve the operational reliability and economy of the integrated energy system, a rolling optimization planning framework and model of an integrated energy system considering compressed air energy storage and sliding time window-based electric and heating integrated response demand is proposed, which can obtain both optimal resource configuration and energy management strategy. The planning problem is to coordinate the output of various dispatching resources on the basis of satisfying the constraints, so as to minimize the total cost. Taking into account the uncertainty of wind power and photovoltaic output and the unplanned outage risk of the unit, a stochastic optimization model is constructed based on the probability density function of the risk variable, and finally the cost probability distribution is obtained. The results verify the universality of the planning model. The comparison of five different energy system configuration schemes and multiple time window length schemes verifies the validity and superiority of the model. The simulation results show that the integrated energy system scheme proposed by this planning model has better economy than the scheme without compressed air energy storage, and the operating cost decreases by 11.9% and the total cost decreases by 4.5%. In addition, the results show that through the introduction of demand response, the operating economy and flexibility of the integrated energy system are improved, and as the sliding window length increases, the total cost gradually decreases. • A rolling optimization planning framework and model of an IES considering CAES and sliding time window is proposed. • Integrated demand response (DR) is introduced into IES model. • The comparison of five different energy system configuration schemes verifies the validity and superiority of the model. • The operation characteristics of electricity-thermal joint storage and joint supply of CAES can improve the economy of IES. • A stochastic optimization model of IES is constructed based on Monte Carlo simulation. [ABSTRACT FROM AUTHOR]