Distributed Stochastic Market Clearing with High-Penetration Wind Power

Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this paper based on the DC optimal power flow (OPF). The objective is to minimize the social cost which consists of conventional generation costs, end-user disutility, as well as a risk measure of the system re-dispatching cost. Capitalizing on the conditional value-at-risk (CVaR), the novel model is able to mitigate the potentially high risk of the recourse actions to compensate wind forecast errors. The resulting convex optimization task is tackled via a distribution-free sample average based approximation to bypass the prohibitively complex high-dimensional integration. Furthermore, to cope with possibly large-scale dispatchable loads, a fast distributed solver is developed with guaranteed convergence using the alternating direction method of multipliers (ADMM). Numerical results tested on a modified benchmark system are reported to corroborate the merits of the novel framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9 figure

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework

A review of co-optimization approaches for operational and planning problems in the energy sector

This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Enhanced Reserve Procurement Policies for Power Systems with Increasing Penetration Levels of Stochastic Resources

abstract: The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost. Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements. This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

A Reserve-Based Method for Mitigating the Impact of Renewable Energy

The fundamental operating paradigm of today\u27s power systems is undergoing a significant shift. This is partially motivated by the increased desire for incorporating variable renewable energy resources into generation portfolios. While these generating technologies offer clean energy at zero marginal cost, i.e. no fuel costs, they also offer unique operating challenges for system operators. Perhaps the biggest operating challenge these resources introduce is accommodating their intermittent fuel source availability. For this reason, these generators increase the system-wide variability and uncertainty. As a result, system operators are revisiting traditional operating strategies to more efficiently incorporate these generation resources to maximize the benefit they provide while minimizing the challenges they introduce. One way system operators have accounted for system variability and uncertainty is through the use of operating reserves. Operating reserves can be simplified as excess capacity kept online during real time operations to help accommodate unforeseen fluctuations in demand. With new generation resources, a new class of operating reserves has emerged that is generally known as flexibility, or ramping, reserves. This new reserve class is meant to better position systems to mitigate severe ramping in the net load profile. The best way to define this new requirement is still under investigation. Typical requirement definitions focus on the additional uncertainty introduced by variable generation and there is room for improvement regarding explicit consideration for the variability they introduce. An exogenous reserve modification method is introduced in this report that can improve system reliability with minimal impacts on total system wide production costs. Another potential solution to this problem is to formulate the problem as a stochastic programming problem. The unit commitment and economic dispatch problems are typically formulated as deterministic problems due to fast solution times and the solutions being sufficient for operations. Improvements in technical computing hardware have reignited interest in stochastic modeling. The variability of wind and solar naturally lends itself to stochastic modeling. The use of explicit reserve requirements in stochastic models is an area of interest for power system researchers. This report introduces a new reserve modification implementation based on previous results to be used in a stochastic modeling framework. With technological improvements in distributed generation technologies, microgrids are currently being researched and implemented. Microgrids are small power systems that have the ability to serve their demand with their own generation resources and may have a connection to a larger power system. As battery technologies improve, they are becoming a more viable option in these distributed power systems and research is necessary to determine the most efficient way to utilize them. This report will investigate several unique operating strategies for batteries in small power systems and analyze their benefits. These new operating strategies will help reduce operating costs and improve system reliability

Towards flexibility trading at TSO-DSO-customer levels : a review

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.fi=vertaisarvioitu|en=peerReviewed

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

Simultaneous scheduling of multiple frequency services in stochastic unit commitment

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimising the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimisation with frequency dynamics. This paper proposes a novel frequency-constrained Stochastic Unit Commitment (SUC) model which, for the first time, co-optimises energy production along with the provision of synchronised and synthetic inertia, Enhanced Frequency Response (EFR), Primary Frequency Response (PFR) and a dynamically-reduced largest power infeed. The contribution of load damping is modelled through a linear inner approximation. The effectiveness of the proposed model is demonstrated through several case studies for Great Britain’s 2030 power system, which highlight the synergies and conflicts among alternative frequency services, as well as the significant economic savings and carbon reduction achieved by simultaneously optimising all these services