Combined hydro-wind generation bids in a pool-based electricity market

Present regulatory trends are promoting the irect participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment from the wind generators. However, wind resources are uncertain, and the final power delivered usually differs from the initial power committed. This imbalance produces an overcost in the system, which must be paid by those who produce it, e.g., wind generators among others. As a result, wind farm revenue decreases, but it could increase by allowing wind farms to submit their bids to the markets together with a hydro generating unit, which may easily modify its production according to the expected imbalance. This paper presents a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction.En prens

The impact of electricity storage on wholesale electricity prices

This paper analyzes the impact of electricity storage on the production cost of a power system and the marginal cost of electricity (electricity price) using a unit commitment model. Also real world data has been analyzed to verify the e®ect of storage operation on the electricity price using econometric techniques. The unit commitment model found that the deployment of a storage system reduces the fuel cost of the power system but increases the average electricity price through its e®ect on the power system operation. However, the reduction in the production cost was found to be less than the increase in the consumer's cost of electricity resulting in a net increase in costs due to storage. Di®erent storage and CO2 price scenarios were investigated to study the sensitivity of these results. The regression analysis supports the unit commitment results and ¯nds that the presence of storage increases average wholesale electricity prices for the case study system.Electricity storage; Electricity price; Production cost

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Ellipsoidal Prediction Regions for Multivariate Uncertainty Characterization

While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times and variables of interest. Such dependencies are key in a large share of operational problems involving renewable power generation, load and electricity prices for instance. The few methods that account for dependencies translate to sampling scenarios based on given marginals and dependence structures. However, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of polyhedra or ellipsoids. Consequently, we propose a systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined probability and minimum volume. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for misspecification of ellipsoidal prediction regions. Application results based on three datasets with wind, PV power and electricity prices, allow us to assess the skill of the resulting ellipsoidal prediction regions, in terms of calibration, sharpness and overall skill.Comment: 8 pages, 7 Figures, Submitted to IEEE Transactions on Power System

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Power system flexibility improvement with a focus on demand response and wind power variability

Unpredictable system component contingencies have imposed vulnerability on power systems, which are under high renewables penetration nowadays. Intermittent nature of renewable energy sources has made this unpredictability even worse than before and calls for excellent adaptability. This paper proposes a flexible security-constrained structure to meet the superior flexibility by coordination of generation and demand sides. In the suggested model, demand-side flexibility is enabled via an optimum real-time (RT) pricing program, while the commitment of conventional units through providing up and down operational reserves improves the flexibility of supply-side. The behaviour of two types of customers is characterized to define an accurate model of demand response and the effect of customers' preferences on the optimal operation of power networks. Conclusively, the proposed model optimizes RT prices in the face of contingency events as well as wind power penetration. System operators together with customers could benefit from the proposed method to schedule generation and consumption units reliably.fi=vertaisarvioitu|en=peerReviewed

The Role of Community Values in Wind Energy Development: Exploring the Benefits and Applications of Community Wind for Reducing Local Opposition to Wind Energy Systems

Worldwide, wind energy generation is growing rapidly as a cleaner and less invasive alternative to traditional fossil-fuel energy sources. Yet, in the United States, the advancement of wind energy has been stunted by three factors: (1) the uncertainty of the federal Production Tax Credit; (2) the lack of transmission lines connecting wind projects to electricity grids; and (3) enduring local cultural and aesthetic objections to wind turbines. Frustrated with the imbalanced allocation of costs and benefits imposed by most wind energy projects, some individuals and municipalities have deployed zoning laws, nuisance claims, or environmentalist arguments to discourage wind energy development in their area. “Community wind” is a model of wind energy generation that improves residents’ perception of turbines by using local ownership, services and utility grids to concentrate the economic benefits of wind power in the communities that produce it. This paper sets forth a proposal for applying the community wind model in a suburban context, through the mechanism of the homeowner’s association (HOA). HOAs are uniquely situated to implement community wind to lower their energy costs, provide affordable housing, enhance local schools, and shift Americans’ perception of wind farms in a more positive direction

Massachusetts Offshore Wind Future Cost Study

The Special Initiative on Offshore Wind is an independent project at the University of Delaware's College of Earth, Ocean and Environment that supports the advancement of offshore wind as part of a comprehensive solution to the most pressing energy problems facing the United States.  The Special Initiative on Offshore Wind provides expertise, analysis, information sharing, and strategic partnership with industry, advocacy and government stakeholders to build understanding and drive the deployment of offshore wind

Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively