Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modeled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets Assisted by CPS

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy assisted by a cyber-physical system for supporting management decisions to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a stochastic linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modelled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

A multivariate approach to bidding strategies

A multivariate approach to bidding strategy is presented in comparison with previous standard approaches. An optimal formulation is derived and a method of parameter estimation proposed. A case study illustrates the derivation of optimal and other strategic mark up values against a single bidder. Concluding remarks concern extensions to multiple competitors differing levels of information, and sensitivity analysis.

Investigation on the development of bidding strategies for a wind farm owner

In this paper, the development of bidding strategies is investigated for a wind farm owner. The optimization model is characterized by making the analysis of scenarios. The proposed approach allows evaluating alternative production strategies in order to submit bids to the electricity market with the goal of maximizing profits. The problem is formulated as a linear programming problem. An application to a case study is presented

Game Theoretic Bidding Strategies for Auctions in Green Electricity Markets

Electricity markets in the United States presently employ an auction mechanism to determine the dispatch of power generation units. In this market design, generators submit bid prices to a regulation agency for review, and the regulator conducts an auction selection in such a way that satisfies electricity demand. Most regulators currently use an auction selection method that minimizes total offer costs ["bid cost minimization" (BCM)] to determine electric dispatch. However, recent literature has shown that this method may not minimize consumer payments, and it has been shown that an alternative selection method that directly minimizes total consumer payments ["payment cost minimization" (PCM)] may benefit social welfare in the long term. The objective of this project is to further investigate the long term benefit of PCM implementation and determine whether it can provide lower costs to consumers. The two auction selection methods are expressed as linear constraint programs and are implemented in an optimization software package. Methodology for game theoretic bidding simulation is developed using EMCAS, a real-time market simulator. Results of a 30-day simulation showed that PCM reduced energy costs for consumers by 12%. However, this result will be cross-checked in the future with two other methods of bid simulation as proposed in this paper.

Optimal Wind Bidding Strategies in Day-Ahead Markets

This paper presents a computer application for wind energy bidding in a day-ahead electricity market to better accommodate the variability of the energy source. The computer application is based in a stochastic linear mathematical programming problem. The goal is to obtain the optimal bidding strategy in order to maximize the revenue. Electricity prices and financial penalties for shortfall or surplus energy deliver are modeled. Finally, conclusions are drawn from an illustrative case study, using data from the day-ahead electricity market of the Iberian Peninsula.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

The iso-Score Curve Graph. A new tool for competitive bidding

The present work describes a new tool that helps bidders improve their competitive bidding strategies. This new tool consists of an easy-to-use graphical tool that allows the use of more complex decision analysis tools in the field of Competitive Bidding. The graphic tool described here tries to move away from previous bidding models which attempt to describe the result of an auction or a tender process by means of studying each possible bidder with probability density functions. As an illustration, the tool is applied to three practical cases. Theoretical and practical conclusions on the great potential breadth of application of the tool are also presented.

Coordinated views to assist exploration of spatio-temporal data: a case study

In this paper we focus on providing coordinated visual strategies to assist users in performing tasks driven by the presence of temporal and spatial attributes. We introduce temporal visualization techniques targeted at such tasks, and illustrate their use with an application involving a climate classification process. The climate classification requires extensive Processing of a database containing daily rain precipitation values collected along over fifty years at several spatial locations in the São Paulo state, Brazil. We identify user exploration tasks typically conducted as part of the data preparation required in this process, and then describe how such tasks may be assisted by the multiple visual techniques provided. Issues related to the use of the multiple techniques by an end-user are also discussed.

A multi-hierarchical strategy for on-ramp coordination

Ramp metering (RM) is an access control for motorways, in which a traffic signal is placed at on-ramps to regulate the rate of vehicles entering the motorway and thus to preserve the motorway capacity. In general, RM algorithms fall into two categories by their effective scope: local control and coordinated control. Local control algorithm determines the metering rate based on the traffic condition on adjacent motorway mainline and the on-ramp. Conversely, coordinated RM strategies make use of measurements from the entire motorway network to operate individual ramp signals for optimal performance at the network level. This study proposes a multi-hierarchical strategy for on-ramp coordination. The strategy is structured in two layers. At the higher layer, a centralised, predictive controller plans the coordination control within a long update interval based on the location of high-risk breakdown flow. At the lower layer, reactive controllers determine the metering rates of those ramps involved in the ramp coordination with a short update interval. This strategy is modelled and applied to the northbound model of the Pacific Motorway in a micro-simulation platform (AIMSUN). The simulation results show that the proposed strategy effectively delays the onset of congestion and reduces total congestion with better managed on-ramp queues.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

A decision- theoretic model for M&A sale processes

A Work Project, presented as part of the requirements for the Award of a Masters Double Degree in Economics and International Business from the NOVA – School of Business and Economics and Insper Instituto de Ensino e Pesquisa

Spatial Inequalities and Social Utility: Mobility Efforts and Accessibility in Greater Santiago

Emergent phenomena such as urban sprawl, travel intensification and loss of cohesion in contemporary metropolises, impose stronger constraints on its inhabitants. Among them, travel and location capabilities become a fundamental factor of social integration and a multiplier of income inequalities. The simultaneous analysis of housing-travel efforts and accessibility to urban opportunities in Greater Santiago shows that these dimensions are closely related and exert an important influence on spatial mobility and inequalities among its inhabitants. Furthermore, a theoretical model of displacements, considering income and location, confirms the importance of proximity and non-motorized transport in order to optimize daily mobility strategies of households. Overall, the empirical and theoretical results presented show the need to implement coordinated planning strategies between the housing and transport sectors, addressing not only travel acceleration, but mainly the consistency between accommodation and opportu  ties location. The creation of such planning tools could be a more sustainable alternative than current growth trends in Greater Santiago.

Contractor key competitiveness indicators: A China study

A proper method to assess contractor competitiveness is important both for assisting clients in the selection of proper contractors and for assisting contractors in the development of more competitive bidding strategies. Previous studies have identified various indicators for assessing contractor competitiveness, and several assessment methods have been introduced. Nevertheless, these studies are limited because they are unable to tell which indicators are more important in different market environments. This paper identifies the key competitiveness indicators �KCIs� for assessing contractor competitiveness in the Chinese construction market. An index value is used to indicate the relative significance of various competitiveness indicators based on which KCIs are identified. The data applied in this study are from a survey of the construction industry in mainland China. The research findings provide valuable information for both existing businesses and the construction professionals who plan to compete for construction works in the Chinese market. The study provides useful references for further studies that compare the KCIs used in the Chinese construction industry and those used in other construction industries.