Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great exten

Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory

© 2019 Elsevier Ltd An energy hub is a multi-carrier energy system that is capable of coupling various energy networks. It increases the flexibility of energy management and creates opportunities to increase the efficiency and reliability of energy systems. When plug-in hybrid electric vehicles (PHEVs)are incorporated into the energy hub, batteries can act as an aggregated storage system, increasing the potential integration of variable renewable energy sources (RES)into power system networks. This paper presents a new model for the optimal operation of an energy hub that includes RES, PHEVs, fuel cell vehicles, a fuel cell, an electrolyzer, a hydrogen tank, a boiler, an inverter, a rectifier, and a heat storage system. A novel model is developed to estimate the uncertainty associated with the power consumption of PHEVs during trips using information gap decision theory (IGDT)under risk-averse and risk-seeking strategies. Simulation results demonstrate that the proposed method maximizes the objective function under the risk-neutral and risk-averse strategies, while minimizing the objective function under the risk-seeking strategy. Results from the modeling show that considering the uncertainty associated with the power consumption of PHEVs using IGDT enables the energy hub operator to make appropriate decisions when optimizing the operation of the energy hub against possible changes in power consumption of PHEVs

Day-ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach

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Sustainable Energy Options for Austin Energy

During the 2008-2009 academic year the City of Austin, on behalf of Austin Energy (AE), and Solar Austin co-funded a policy research project to review options for AE to achieve sustainable energy generation and become carbon neutral by 2020. This project developed methods to evaluate future power generation options for their feasibility and cost-effectiveness. The report evaluates different power generation technology options as well as demand-side management and other AE investment options to discourage future energy use and meet future projected energy demand. The project team assessed scenarios of alternate investments that could be made between 2009 and 2020 that would allow AE to produce and distribute the electricity its customers demand at a reasonable cost while reducing carbon dioxide emissions. This report describes a set of short-term and long-term investment options that can help AE, its customers, and be of use for developing sustainable electric utilities nationwide. The report comprises three volumes: volume I is a summary; volume II discusses energy demand and the characteristics of available power generation technologies; and volume III is a "Future Resource Portfolio Analysis" which evaluates eight energy portfolio scenarios in detail. This project was funded in part by the IC² Institute.IC2 Institut

Energy Systems Analysis and Modelling towards Decarbonisation

The Paris Agreement establishes a process to combine Nationally Determined Contributions with the long-term goal of limiting global warming to well below 2 °C or even to 1.5 °C. Responding to this challenge, EU and non-EU countries are preparing national and regional low-emission strategies outlining clean energy-transition pathways. The aim of this book is to provide rigorous quantitative assessment of the challenges, impacts and opportunities induced by ambitious low-emission pathways. It aims to explore how deep emission reductions can be achieved in all energy supply and demand sectors, exploring the interplay between mitigation options, including energy efficiency, renewable energy uptake and electrification, for decarbonising inflexible end-uses such as mobility and heating. The high expansion of renewable energy poses high technical and economic challenges regarding system configuration and market organisation, requiring the development of new options such as batteries, prosumers, grid expansion, chemical storage through power-to-X and new tariff setting methods. The uptake of disruptive mitigation options (hydrogen, CCUS, clean e-fuels) as well as carbon dioxide removal (BECCS, direct air capture, etc.) may also be required in the case of net-zero emission targets, but raises market, regulatory and financial challenges. This book assesses low-emission strategies at the national and global level and their implications for energy-system development, technology uptake, energy-system costs and the socioeconomic and industrial impacts of low-emission transitions

Consumer-centric community electricity markets with agents' utility maximization: robust approaches and analysis of ancillary services

The flexibility of the end-users in the electricity markets is becoming more pertinent with the evolution of market mechanisms allowing consumers to participate actively. The advent of Distributed Energy Resources (DERs) and energy storage systems is gradually and continuously changing the roles of market operators. In the emerging consumer-centric markets, the consumers are equipped with DERs and can participate actively as prosumers, trading their energy resources with neighbours. The other community agents are mainly consumers without DERs and producers without load demands. The impact of the uncertainty of DERs and load demands on community-based electricity market (CBEM) structures has not been fully investigated. In this thesis, we propose a robust solution to CBEM operations under uncertainty and compare the optimal decisions on energy trades with deterministic, and opportunistic models. Also, we employ Taguchi's orthogonal array testing (TOAT) to generate proficient scenarios from uncertain parameters of prosumers', producers', and consumers' resources. While the optimality of the solution provided by the CBEM mechanisms has been analyzed extensively, the ability to address the individual user preferences that would maximize their utility has been hard to incorporate. We also, extend the traditional, community-based, centralized electricity market to incorporate the consumer and producer preferences relating to economic aspects rather than technical constraints. This is achieved with the use of indifference curves for standard utility functions used in exchange economy (such as Cobb-Douglas utility, perfect substitutes, perfect complements, etc.) This thesis further proposes a single-stage robust formulation of the traditional, community-based, centralized electricity market incorporating the agents' preferences relating to economic aspects rather than technical constraints. A single-stage robust optimization model of the CBEM with utility maximization is formulated by integrating uncertainty constraints defined within polyhedral uncertainty sets representing variations in agents' resources from forecasted/expected values. The proposed approach ensures the robustness of the market with the uncertainty of agents' generation and load resources. It allows community agents the ability to adjust their budgets to the given robust scenario. Thereafter the proposed methodology can control the degree of robustness as regards the uncertainty parameters of agents' resources. With consideration to the emerging consumer-centric markets, the possibilities of the offer of ancillary services besides demand-side responses, energy management, and peak shaving/shifting/leveling functions are being explored in providing flexibility and scalability in the market mechanism. The offer of these services within several market mechanisms and entities has been researched widely in literature, in our work we propose the formulation of the CBEM in a joint day ahead market model offering ancillary services of reserve and regulation while minimizing the total traded costs of agents' and the community manager and also maximizing the individual utility functions of agents. Finally, a single-stage robust mixed-integer linear problem is presented which models the joint market over the worst-case realisations of uncertain parameters of agents' resources and reserve/regulation prices represented within polyhedral uncertainty sets. In this work, the performance of the proposed CBEM market is implemented in three case studies with consideration to different market participants the prosumers, producers, and consumers to analyse the impact of uncertainty in CBEM with and without agent utility maximization and also in a joint day-ahead market offering ancillary services. The first case study presents 7 prosumers equipped with PV generation and load consumption, the second case study presents 15 agents with 7 producers equipped with PV generation and 8 consumers and the third case study presents 5 prosumers equipped with PV generation, 20 consumers, and three producers one with a wind production and the other two with conventional generation. Simulation results demonstrate the costs of robustness as a result of the impact of uncertainty, the agents' preference relations and utility maximization, and the total profits in the offer of ancillary services