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

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

Day-Ahead Offering Strategy In The Market For Concentrating Solar Power Considering Thermoelectric Decoupling By A Compressed Air Energy Storage

Due to limited fossil fuel resources, a growing increase in energy demand and the need to maintain positive environmental effects, concentrating solar power (CSP) plant as a promising technology has driven the world to find new sustainable and competitive methods for energy production. The scheduling capability of a CSP plant equipped with thermal energy storage (TES) surpasses a photovoltaic (PV) unit and augments the sustainability of energy system performance. However, restricting CSP plant application compared to a PV plant due to its high investment is a challenging issue. This paper presents a model to assemble a combined heat and power (CHP) with a CSP plant for enhancing heat utilization and reduce the overall cost of the plant, thus, the CSP benefits proved by researches can be implemented more economically. Moreover, the compressed air energy storage (CAES) is used with a CSP-TES-CHP plant in order that the thermoelectric decoupling of the CHP be facilitated. Therefore, the virtual power plant (VPP) created is a suitable design for large power grids, which can trade heat and electricity in response to the market without restraint by thermoelectric constraint. Furthermore, the day-ahead offering strategy of the VPP is modeled as a mixed integer linear programming (MILP) problem with the goal of maximizing the profit in the market. The simulation results prove the efficiency of the proposed model. The proposed VPP has a 2% increase in profit and a maximum 6% increase in the market electricity price per day compared to the system without CAES

Optimal operation strategies of multi-energy systems integrated with liquid air energy storage using information gap decision theory

In this paper, a framework of multi-energy system (MES) integrating with a liquid air energy storage (LAES) system was proposed. LAES, where liquid air works as an energy storage media, is a powerful and eco-friendly technology for storing renewable energy resources and reducing grid curtailment. Considering the characteristics of LAES (i.e. cold and heat circulation), the incorporation of LAES system into the Combined Cooling, Heating and Power system can achieve integrated use of energy and effectively save energy. Moreover, the prices of electricity will affect the overall cost of the MES. In other words, the decision-makers of the MES need to consider the uncertainty of electricity prices when making power dispatching decisions. To model the uncertainty of electricity prices, the information gap decision theory method was used to study power dispatching strategies of the MES. Three different strategies were proposed, including risk-neutral, risk-averse and risk-taker. In addition, demand response algorithms were used to study load transfer strategies. The results show that the demand responses of the three strategies are effective in terms of load transfer and cost saving. The total operation cost in the risk-neutral strategy with demand response can be 6.82% less than that without demand response; In the risk-taker strategy with demand response, the allowable grid electricity price is reduced by 25.24% when the opportunity cost drops by $8,000, and 23.32% without demand response. With additional robustness cost, the acceptable price change ratio using demand response is 21.91% in the risk-averse strategy, and 20.04% without demand response

Optimal scheduling and management of pumped hydro storage integrated with grid-connected renewable power plants

Pumped hydro-energy storage will become a fundamental element of power systems in the coming years by adding value to each link in electricity production and the supply chain. The growth of these systems is essential for improving the integration of renewables and avoiding dependence on fossil fuel sources, such as gas or oil. This paper presents the modeling and application of an optimal hourly management model of grid-connected photovoltaic and wind power plants integrated with reversible pump-turbine units to maximize the monthly operating profits of the energy system and meet electricity demand. The techno-economic dispatch model is formulated as a mixed-integer optimization problem. To assess the proposed model, it is applied to a Spanish case study system, and the results are obtained for an entire year. The combination of renewable energy and pumped hydro energy storage reduces energy dependence by decreasing energy costs by 27 % compared with a system without storage to satisfy the required electricity demand. The findings confirm that storage plays a key role in energy transition to ensure the security and stability of power systems with a higher share of renewable generation

On the decarbonization of chemical and energy industries: Power-to-X design strategies

Tesis por compendio de publicaciones[ES]Hoy en día, la preocupación por la sostenibilidad está dando lugar a todo un nuevo sistema económico. Este nuevo paradigma afecta a todos los sectores como la agricultura, la industria, el sector financiero, etc. Dos de los más afectados son la industria química y el sistema energético debido a su configuración actual y, estos dos sectores son particularmente estudiados en esta tesis. En cuanto a la industria química, la producción electroquímica es uno de los métodos más atractivos para producir productos químicos de forma sostenible dejando atrás la producción tradicional no renovable. En esta tesis se ha prestado especial atención a la producción sostenible de amoníaco. Se han evaluado dos rutas diferentes, la primera utiliza la electrólisis del agua y evalúa diferentes tecnologías de separación del aire en función de la escala, y la segunda utiliza la biomasa como materia prima. Utilizando estos productos electroquímicos, es posible construir una nueva industria química sostenible. En esta tesis se propone la síntesis de carbo- nato de dimetilo (DMC) utilizando metanol renovable, amoníaco y dióxido de carbono capturado. En cuanto al sector energético, la introducción de fuentes renovables es esencial para alcanzar los objetivos propuestos. En este punto, el almacenamiento de energía será crucial para garantizar la satisfacción de la demanda debido a las fluctuaciones inherentes a las energías solar y eólica. Esta tesis se centra en la evaluación de productos químicos como forma potencial de almacenamiento o como vectores de energía. Se estudia la transformación del amoníaco en electricidad a escala de proceso proporcionando los resultados necesarios para implementar esta alternativa a escala de red. El diseño y el funcionamiento de las insta- laciones basadas en renovables se abordan simultáneamente, incluyendo la ubicación de las unidades debido a que los recursos renovables estan distri- buidos. Se propone un sistema integrado para utilizar productos químicos como vectores energéticos para diferentes aplicaciones energéticas en una región de España, calculando las capacidades, la operación y la ubicación óptima de las instalaciones. Además, se realiza la integración de diferentes energías renovables intermitentes y no intermitentes junto con diferentes tecnologías de almacenamiento desde una perspectiva económica y social para satisfacer una determinada demanda eléctrica. Todos estos sistemas y herramientas propuestos contribuyen a crear un escenario futuro en el que los sectores químico y energético se transforman para ser menos impactantes en el medio ambiente que nos rode

An Accurate Bilinear Cavern Model for Compressed Air Energy Storage

Compressed air energy storage is suitable for large-scale electrical energy storage, which is important for integrating renewable energy sources into electric power systems. A typical compressed air energy storage plant consists of compressors, expanders, caverns, and a motor/generator set. Current cavern models used for compressed air energy storage are either accurate but highly nonlinear or linear but inaccurate. The application of highly nonlinear cavern models in power system optimization problems renders them computationally challenging to solve. In this regard, an accurate bilinear cavern model for compressed air energy storage is proposed in this paper. The charging and discharging processes in a cavern are divided into several real/virtual states. The first law of thermodynamics and ideal gas law are then utilized to derive a cavern model, i.e., a model for the variation of temperature and pressure in these processes. Thereafter, the heat transfer between the air in the cavern and the cavern wall is considered and integrated into the cavern model. By subsequently eliminating several negligible terms, the cavern model reduces to a bilinear model. The accuracy of the bilinear cavern model is verified via comparison with both an accurate nonlinear model and two sets of field-measured data. The bilinear cavern model can be easily linearized and is then suitable for integration into optimization problems considering compressed air energy storage. This is verified via comparatively solving a self-scheduling problem of compressed air energy storage using different cavern models.Comment: 18 pages, 15 figures, accepted by Applied Energy on March 201

A MILP model for revenue optimization of a compressed air energy storage plant with electrolysis

Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market prices, a rolling horizon approach was implemented. The resulting revenues ranged between EUR 0.5 Mio and EUR 7 Mio per year and suggested that an economically sound operation of the storage plant is possible

Optimal Scheduling of Energy Storage for Energy Shifting and Ancillary Services to the Grid

This thesis is mainly focused on developing optimization-based models for scheduling of energy storage units. At first, a real-time optimal scheduling algorithm is developed seeking to maximize the storage revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices. The electricity price modulation is proposed as an approach to competitively offer incentive by the utility regulator to storage to fill the gap between current and a stable rate of return. Subsequently, the application of large-scale storage for congestion relief in transmission systems as an ancillary service to the grid is investigated. An algorithm is proposed for the following objectives: (i) to generate revenue primarily by exploiting electricity price arbitrage opportunities and (ii) to optimally prepare the storage to maximize its contribution to transmission congestion relief. In addition, an algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by Independent System Operators (ISOs). While the operation of locally controlled storage is optimally scheduled at the owner’s end, using the proposed algorithm, storage is fully dispatchable at the ISO’s end. Finally, a model is proposed and analyzed to aggregate storage benefits for a large-scale load. The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed. The applications of the proposed algorithms and models are examined using real-world market data adopted from Ontario’s electricity market and actual load information from a large-scale institutional electricity consumer in Ontario