Self-Scheduling Operations of a Compressed Air Energy Storage Facility Under Uncertainties

High penetration of Renewable Energy Sources (RES) such as solar and wind in power systems reduce carbon emissions and decentralize the energy generation. However, the intermittency of these sources introduces new challenges, since solar power is only available during sunlight hours and wind power is difficult to forecast and may present a high variability. Thus, since RES generation is not dispatchable, a power system with large RES penetration may not meet the demand at peak hours and experience voltage flickers and frequency fluctuations. To tackle these challenges, various Energy Storage Systems (ESS) technologies have been developed and deployed at different scales throughout the grid, providing either energy arbitrage or frequency regulation services to the power system. For the former, there are two mature large-scale ESS: pumped hydro storage and Compressed Air Energy Storage (CAES), with the latter being less restrictive in terms of location. Despite being a mature technology, there are only two large-scale CAES facilities worldwide. However, with the challenges modern power systems face nowadays, the bulk capacity, fast response, and efficiency of CAES facilities makes them an attractive ESS alternative. Electricity prices vary throughout the day depending on the system demand. At low demand, low-cost generators operate, resulting in cheaper electricity; at peak demand, more expensive units operate, hence increasing electricity prices. These electricity price variations opens opportunities for pro table businesses. In this context, an ESS facility owned by a private investor, depending on its capacity compared to the overall system, may participate as a price-taker or as a price-maker in electricity markets. Due to its bulk capacity, CAES can provide energy arbitrage to the grid and participate in the energy and reserve markets. Also, CAES newer designs decouple the charging and discharging processes using two synchronous machines, providing enhanced frequency regulation services. Since a CAES facility presents physical limitations, its optimum daily schedule must be determined a priori. Given the day-ahead electricity price forecast, an optimum schedule can be determined through self-scheduling models, where the daily pro t of the facility is maximized, which requires that the facility be properly modeled. Furthermore, with the high penetration of RES new and large sources of uncertainty have been introduced, particularly in generation and real-time market prices. Therefore, these uncertainties must be properly considered in CAES modeling and operation. In this thesis self-scheduling models for a price-taker CAES facility, that partakes in energy and reserve markets under electricity price uncertainties, are proposed. Using an existing non-linear model for a CAES facility, Robust Optimization (RO) is employed to represent price uncertainties, yielding an optimum schedule that protects against the worst-case scenario for a given level of conservatism. The model is benchmarked with Monte Carlo Simulations (MCS), presenting a lower computational burden while computing scenarios that the MCS fails to obtain. Thereafter, a novel linear thermodynamic model for the CAES is proposed, using mathematical tools for linearization such as McCormick Envelopes and linear-piecewise approximation, which compared with an existing non-linear model, it yields similar results at significantly lower computational costs. The novel model is further expanded considering uncertainties in electricity prices using RO and Affine Arithmetic (AA) approaches. The AA method keeps track of correlated uncertainties, yielding an optimum range of schedule with adjustable power dispatch for given real-time mismatches in price forecasts. Both methods are compared and benchmarked with the MCS approach, presenting significantly lower computational costs, with pro t intervals obtained from AA being more conservative than MCS and RO, i.e., the former method envelops the intervals obtained from the latter techniques. The CAES pro t and schedules for different levels of initial and fi nal State of Charge (SOC) of the facility are then assessed in order to estimate an ideal SOC level where the facility may maximize its participation and pro fit. Finally, a Principal Components Analysis (PCA)-Affine Policy (AP)-based self scheduling model for the CAES facility is proposed. PCA is a knowledge extraction based mathematical tool to reduce the dimension of a mathematical model by removing the less relevant variables, which may decrease the accuracy of the model. The method of AP, similar to AA, keeps track of correlated uncertainties and provides an optimum range of schedule with adjustable power dispatch for real-time mismatches in price forecast. The PCA-AP model is compared with AA and MCS, which is computational more expensive compared with AA, provides a tighter interval of pro t, hence ensuring a safer margin of operation in pessimistic scenarios. Compared with the MCS, similar results were obtained at a lower computational cost. The operation of a CAES facility charging and discharging concurrently is then examined, which offers the facility with a larger set of combinations for its operational states, and hence greater pro t, but at increased computational costs

Compressed Air Energy Storage: Modelling & Applications for Sustainable Electric Power Systems

With the increasing concerns about the climate change and depletion of non-renewable energy sources, there has been a growing emphasis on the deployment of renewable energy sources in electric power systems. However, due to inherent stochasticity of renewable energy sources, this transition toward sustainable electric power systems creates serious challenges for the reliable and safe operation of such systems. Large-scale energy storage systems are considered to be key enablers for integrating increasing penetration of renewable energy sources by adding flexibility to the electric power systems. This thesis investigates compressed air energy storage (CAES) as a cost-effective large-scale energy storage technology that can support the development and realization of sustainable electric power systems. Firstly, this thesis develops a novel planning framework of CAES to consider its benefits from an electric utility’s perspective. The proposed framework is used to investigate different applications of CAES which depend upon the location and size of CAES in an electric power system. The proposed framework also considers the option of installing a dynamic thermal line rating (DTLR) system which measures real-time, maximum power ratings of transmission lines. Next, this thesis examines the existing models of CAES employed in electric power system studies and proposes a novel thermodynamic-based model of CAES which is more accurate yet suitable for electric power system studies. The importance and significance of the proposed model is established through its application in the problem of optimal scheduling of CAES in electricity markets. It is demonstrated that through the proposed model, the operator of a CAES can submit bids in electricity markets without violating any of the technical constraints of CAES. Lastly, this thesis inspects the reliability benefits of CAES to an electric power system. In this part of the thesis, a four-state reliability model of CAES is developed. The reliability model of CAES is then applied to evaluate the reliability of a wind-integrated electric power system. It is revealed that CAES can significantly improve the reliability indices of an electric power system. Moreover, it is shown that this improvement depends on the location and size of CAES

Risk-oriented multi-area economic dispatch solution with high penetration of wind power generation and compressed air energy storage system

This paper investigates the risk-oriented multi-area economic dispatch (MAED) problem with high penetration of wind farms (WFs) combined with compressed air energy storage (CAES). The main objective is to help system operators to minimize the operational cost of thermal units and CAES units with an appropriate level of security through optimized WF power generation curtailment strategy and CAES charging/discharging control. In the obtained MAED model, several WFs integrated with CAES units are considered in different generation zones, and the probability to meet demand by available spinning reserve during N - 1 security contingency is characterized as a risk function. Furthermore, the contribution of CAES units in providing the system spinning reserve is taken into account in the MAED model. The proposed framework is demonstrated by a case study using the modified IEEE 40-generator system. The numerical results reveal that the proposed method brings a significant advantage to the efficient scheduling of thermal units' power generation, WF power curtailment, and CAES charging/discharging control in the power system.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment

Unit commitment (UC) is one of the most important power system operation problems. To integrate higher penetration of wind power into power systems, more compressed air energy storage (CAES) plants are being built. Existing cavern models for the CAES used in power system optimization problems are not accurate, which may lead to infeasible solutions, e.g., the air pressure in the cavern is outside its operating range. In this regard, an accurate CAES model is proposed for the UC problem based on the accurate bi-linear cavern model proposed in the first paper of this two-part series. The minimum switch time between the charging and discharging processes of CAES is considered. The whole model, i.e., the UC model with an accurate CAES model, is a large-scale mixed integer bi-linear programming problem. To reduce the complexity of the whole model, three strategies are proposed to reduce the number of bi-linear terms without sacrificing accuracy. McCormick relaxation and piecewise linearization are then used to linearize the whole model. To decrease the solution time, a method to obtain an initial solution of the linearized model is proposed. A modified RTS-79 system is used to verify the effectiveness of the whole model and the solution methodology.Comment: 8 page

Economic-Environmental Analysis of Combined Heat and Power-Based Reconfigurable Microgrid Integrated with Multiple Energy Storage and Demand Response Program

Microgrids (MGs) are solutions to integrate high shares of variable renewable energy which can contribute to more economical and environmental benefits, as well as improving the energy supply efficiency. One significant potential of MGs is an expanded opportunity to use the waste heating energy from the conversion of the primary fuel (such as natural gas) to generate electricity. The use of waste heat in combined heat and power (CHP)-based MG is more efficient to meet local load and decrease the emission pollution. Hence, this paper elaborates on optimal multi-objective scheduling of CHP-based MG coupled with compressed air energy storage (CAES), renewable energy, thermal energy storage (TES), and demand response programs through shiftable loads, which considers a reconfiguration capability. The embedded CAES, in addition to the charging/discharging scheme, can operate in a simple cycling mode and serve as a generation resource to supply local load in an emergency condition. The daily reconfiguration of MG will introduce a new generation of MG named reconfigurable microgrid (RMG) that offers more flexibility and enhances system reliability. The RMG is coupled with TES to facilitate the integration of the CHP unit that enables the operator to participate in the thermal market, in addition to the power market. The main intents of the proposed multi-objective problem are to minimize the operation cost along with a reduction in carbon emission. The epsilon-constraint technique is used to solve the multi-objective problem while fuzzy decision making is implemented to select an optimal solution among all the Pareto solutions. The electricity prices and wind power generation variation are captured as random variables in the model and the scenario-based stochastic approach is used to handle them. Simulation results prove that the simultaneous integration of multiple technologies in CHP-based RMG decreases the operation cost and emission up to 3% and 10.28%, respectively

Relevance of Energy Storage Technology in the Development of Solar Power

There is a growing concern in the operating of solar power as a stand-alone and its connection to the grid. This is because; its power quality and sustainability are being affected by intermittency and variability. As the time of the day and the solar intensity changes, the quality of power generation is affected. This has made the use of energy storage inevitable for a quality power generation. This paper considers some existing technologies of energy storage that is applicable in mitigating the challenge facing the deployment of solar power systems. The variability and effectiveness of these storage techniques were considered in terms of technology, efficiency, environmental impact, and response. In conclusion, it was resolved that a self-sufficient solar power system requires appropriate storage techniques to complement its operation. Therefore, understanding the different options of storage is required for a careful selection of the technology which can support the required level of efficiency and effectiveness in electrical energy generation. Keywords: Solar Power, Storage Technologies, Solar Energy, Limitations DOI: 10.7176/JETP/9-3-03 Publication date:March 31st 201

Behind-the-Meter Compressed Air Energy Storage Feasibility and Applications

In many jurisdictions, commercial and industrial (C&I) customers are charged for their energy consumption as well as the power drawn from the grid at peak load hours. In Ontario, the demand-based charge component of the electricity cost has been skyrocketing, and this cost often accounts for a significant portion of the overall operating cost of large customers. The Ontario Government in 2010 launched the Industrial Conservation Initiative (ICI) program which requires large customers (Class A) to pay a Global Adjustment (GA) charge, based on their percentage contribution in load during the top five system peak load hours over a one-year base period. This offers enormous savings opportunity to many industrial customers by using strategies to reduce or offset their load during these system peak load hours. However, managing demand can be challenging when faced with production constraints in areas of high-energy sensitive production lines where short interruptions are not permitted. Energy Storage System (ESS) offers the customer the capability to carry out its usual operations while simultaneously saving on the electricity bill through demand reduction. ESS can provide electricity to the facility during system peak periods to reduce the power drawn from the grid, while during non-peak price periods, the ESS is recharged by harnessing the low-cost power. In this work, a detailed operations model of behind-the-meter Small Scale Compressed Air Energy Storage (SS-CAES) is developed for an industrial customer, with an existing well/cavern that can be re-purposed for air storage. The developed optimization model manages the operation of the CAES facility to minimize electricity costs, determining the storage energy output and the corresponding charging and discharging decisions of the SS-CAES system. Furthermore, a detailed economic analysis is carried out to examine financial viability of a practical behind-the-meter SS-CAES project. Some key parameters such as life cycle, CAES capacity and capital cost, and electricity price are considered for carrying out a sensitivity analysis, and the results suggest that SS-CAES is economically viable in the current Ontario rate structure. It is shown that the cost of an SS-CAES project and GA charges are the key determining factors for economic deployment of SS-CAES in Ontario

Thermo-mechanical energy storage applications for energy system decarbonisation

This research explores the prospective application of thermo-mechanical energy storage technologies for energy system decarbonisation. It characterises, first, the techno-economic performance of one such technologies, Liquid Air Energy Storage (LAES), when operated within the power system to supply energy and reserve services. Then, Liquid Air Energy Storage operation as a multi-energy asset is studied. To conclude, the potential of six between established and novel thermo-mechanical energy storage concepts is cross-compared and benchmarked with incumbent storage technologies for long-duration energy storage applications