Assessment of energy storage systems as a reserve provider in stochastic network constrained unit commitment (original) (raw)
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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.
IEEE Transactions on Power Systems, 2015
We report on the extension of a general stochastic simulation approach for power systems with integrated renewable resources to also incorporate the representation of utility-scale storage resources. The extended approach deploys models of the energy storage resources to emulate their scheduling and operations in the transmission-constrained hourly day-ahead markets. To this end, we formulate a scheduling optimization problem to determine the operational schedule of the controllable storage resources in coordination with the demands and the various supply resources, including the conventional and renewable resources. The incorporation of the scheduling optimization problem into the Monte Carlo simulation framework takes full advantage of the structural characteristics in the construction of the so-called sample paths for the stochastic simulation approach and to ensure its numerical tractability. The extended methodology has the capability to quantify the power system economics, emissions and reliability variable effects over longer-term periods for power systems with the storage resources. Applications of the approach include planning and investment studies and the formulation and analysis of policy. We illustrate the capabilities and effectiveness of the simulation approach on representative study cases on modified IEEE 118 and WECC 240-bus systems. These results provide valuable insights into the impacts of energy storage resources on the performance of power systems with integrated wind resources.
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)
Energy Storage System Analysis Review for Optimal Unit Commitment
Energies, 2019
Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.
Stochastic Procurement of Fast Reserve Services in Renewable Integrated Power Systems
IEEE Access
Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time. INDEX TERMS Critical period, post-contingency actions, stochastic security-constrained unit commitment, reserve services, benders decomposition, energy storage, wind power fluctuations.
Electric Power Systems Research, 2021
This paper presents a method to determine the optimal location, energy capacity, and power rating of distributed battery energy storage systems at multiple voltage levels to accomplish grid control and reserve provision. We model operational scenarios at a one-hour resolution, where deviations of stochastic loads and renewable generation (modeled through scenarios) from a day-ahead unit commitment and violations of grid constraints are compensated by either dispatchable power plants (conventional reserves) or injections from battery energy storage systems. By plugging-in costs of conventional reserves and capital costs of converter power ratings and energy storage capacity, the model is able to derive requirements for storage deployment that achieve the technical-economical optimum of the problem. The method leverages an efficient linearized formulation of the grid constraints of both the HV (High Voltage) and MV (Medium Voltage) grids while still retaining fundamental modeling aspects of the power system (such as transmission losses, effect of reactive power, OLTC at the MV/HV interface, unideal efficiency of battery energy storage systems) and models of conventional generator. A proof-of-concept by simulations is provided with the IEEE 9-bus system coupled with the CIGRE' benchmark system for MV grids, realistic costs of power reserves, active power rating and energy capacity of batteries, and load and renewable generation profile from real measurements.
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.
Operating Reserve Allocation Methods Relative to Energy Unit Commitment
DEStech Transactions on Environment, Energy and Earth Science, 2017
The most distinguished global trend in power systems development in the last decade is by far rapid growth of power generating facilities driven by renewable energy resources. In theory, their primary energy source is a free natural resource such as wind or solar radiation, therefore, their integration should lead to lower marginal costs at wholesale level and therefore lower retail electricity prices payed by end-consumers. The problem lies in their non-dispatchable and stochastic nature. Inability to be dispatched in accordance to power system real-time conditions means that they cannot be committed in the time of need for the power system, i.e., they are unable to provide balancing reserves. Stochasticity of their generation leads to greater need for flexible units capable of changing their operating point, i.e. the greater need for operating reserve units. One can conclude, when the share of renewable energy sources increases, the share of units capable of operating reserves provision decreases, but at the same time the operating reserve requirements increase. Possibilities to solve mentioned problem and to bolster further renewable energy sources integration range from integration of a new flexible conventional units (such as gas power plants), or an energy storage technologies, to usage of renewable units for operating reserves provision despite their lost opportunity cost occurrence. When observing operating reserve's adequacy, it is very important to evaluate the way how it is allocated. With efficient reserve allocation method, the need for additional investments in power system flexibility are deferred and mitigated. This paper provides an insight into different ways of reserve allocation and how does it affect total operating costs of the power system.
Energies, 2017
The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.
Power system operation considering detailed modelling of energy storage systems
International Journal of Electrical and Computer Engineering (IJECE), 2021
The power system operation considering energy storage systems (ESS) and renewable power represents a challenge. In a 24-hour economic dispatch, the generation resources are dispatched to meet demand requirements considering network restrictions. The uncertainty and unpredictability associated with renewable resources and storage systems represents challenges for power system operation due to operational and economical restrictions. This paper developed a detailed formulation to model energy storage systems (ESS) and renewable sources for power system operation in a DCOPF approach considering a 24-hour period. The model is formulated and evaluated with two different power systems (i.e. 5-bus and IEEE modified 24-bus systems). Wind availability patterns and scenarios are used to assess the ESS performance under different operational circumstances. With regard to the systems proposed, there are scenarios in order to evaluate ESS performance. In one of them, the increase in capacity did not represent significant savings or performance for the system, while in the other it was quite the opposite especially during peak load periods. This is an open access article under the CC BY-SA license.