Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Short-term electric market dynamics and generation company decision-making in new deregulated environment

Under the framework developed in [Sheble, 1999a], this work simulates electric market dynamic using systems theory, decision analysis and decision theory. Activities of Generation Companies (GENCOs), the most active players in electric markets, and their impact on market performances are also examined. Decision-making of GENCOs and interactions between them are studied using decision analysis and decision theory.;The first part of this study studies electric market dynamics: dynamics of electricity price, generation output, and other variables. The problem is examined from the viewpoint of an Independent Contract Administrator (ICA) to simulate market performance and GENCOs\u27 activities in different situations. These situations include various interactions among GENCOs (different expectations for competitors adopted by GENCOs), competition types (quantity competition, price competition, both price and quantity competition), market risk levels (decisions under certainty and uncertainty), and different market organizations (with and without certain market information feedback) in the electric market. Decision-making of GENCOs and interactions between them are modeled as control processes and electric markets are modeled as control systems. The corresponding market dynamics is simulated and market dynamic properties are obtained. Simulation results show that interactions between market participants, as well as market risk levels, competition types, and market organizations, are important to market participant\u27s activities and have significant impact on market performances and properties.;The second part of this study is from GENCOs\u27 viewpoint to develop optimal decision-making strategies and models in short term. First of all, GENCOs decision problem in short term in new deregulated environment is identified as a three-dimension problem: how to make optimal decisions for different time in different geographical markets in different service markets to maximize total gain. Then, a new market-based generation scheduling scheme is proposed to solve this problem. Market rules, competitor\u27s activities, uncertainty in the market, bidding strategies, and short-term generation technical constraints are included in the scheme and analyzed using decision analysis and decision theory. Next, Dynamic Programming (DP) and Stochastic Dynamic Programming (SDP) are adopted to solve the new scheduling problems. Results show that in new environment, GENCOs\u27 optimal generation schedules may be very different from schedules proposed in previous work. (Abstract shortened by UMI.

Information requirements for strategic decision making: energy market

Over the last two decades, the electricity sector has been involved in a challenging restructuring process in which the vertical integrated structure (monopoly) is being replaced by a horizontal set of companies. The growing supply of electricity, flowing in response to free market pricing at the wellhead, led to increased competition. In the new framework of deregulation, what characterizes the electric industry is a commodity wholesale electricity marketplace. This new environment has drastically changed the objective of electricity producing companies. In the vertical integrated industry, utilities were forced to meet all the demand from customers living in a certain region at fixed rates. Then, the operation of the Generation Companies (GENCOs) was centralized and a single decision maker allocated the energy services by minimizing total production costs. Nowadays, GENCOs are involved not only in the electricity market but also in additional markets such as fuel markets or environmental markets. A gas or coal producer may have fuel contracts that define the production limit over a time horizon. Therefore, producers must observe this price levels in these other markets. This is a lesson we learned from the Electricity Crisis in California. The Californian market\u27s collapse was not the result of market decentralization but it was triggered by other decisions, such as high natural gas prices, with a direct impact in the supply-demand chain. This dissertation supports generation asset business decisions -from fuel supply concerns to wholesale trading in energy and ancillary services. The forces influencing the value chain are changing rapidly, and can become highly controversial. Through this report, the author brings an integrated and objective perspective, providing a forum to identify and address common planning and operational needs. The purpose of this dissertation is to present theories and ideas that can be applied directly in algorithms to make GENCOs decisions more efficient. This will decompose the problem into independent subproblems for each time interval. This is preferred because building a complete model in one time is practically impossible. The diverse scope of this report is unified by the importance of each topic to understanding or enhancing the profitability of generation assets. Studies of top strategic issues will assess directly the promise and limits to profitability of energy trading. Studies of ancillary services will permit companies to realistically gauge the profitability of different services, and develop bidding strategies tuned to competitive markets

Deep Learning Techniques for Power System Operation: Modeling and Implementation

The fast development of the deep learning (DL) techniques in the most recent years has drawn attention from both academia and industry. And there have been increasing applications of the DL techniques in many complex real-world situations, including computer vision, medical diagnosis, and natural language processing. The great power and flexibility of DL can be attributed to its hierarchical learning structure that automatically extract features from mass amounts of data. In addition, DL applies an end-to-end solving mechanism, and directly generates the output from the input, where the traditional machine learning methods usually break down the problem and combine the results. The end-to-end mechanism considerably improve the computational efficiency of the DL.The power system is one of the most complex artificial infrastructures, and many power system control and operation problems share the same features as the above mentioned real-world applications, such as time variability and uncertainty, partial observability, which impedes the performance of the conventional model-based methods. On the other hand, with the wide spread implementation of Advanced Metering Infrastructures (AMI), the SCADA, the Wide Area Monitoring Systems (WAMS), and many other measuring system providing massive data from the field, the data-driven deep learning technique is becoming an intriguing alternative method to enable the future development and success of the smart grid. This dissertation aims to explore the potential of utilizing the deep-learning-based approaches to solve a broad range of power system modeling and operation problems. First, a comprehensive literature review is conducted to summarize the existing applications of deep learning techniques in power system area. Second, the prospective application of deep learning techniques in several scenarios in power systems, including contingency screening, cascading outage search, multi-microgrid energy management, residential HVAC system control, and electricity market bidding are discussed in detail in the following 2-6 chapters. The problem formulation, the specific deep learning approaches in use, and the simulation results are all presented, and also compared with the currently used model-based method as a verification of the advantage of deep learning. Finally, the conclusions are provided in the last chapter, as well as the directions for future researches. It’s hoped that this dissertation can work as a single spark of fire to enlighten more innovative ideas and original studies, widening and deepening the application of deep learning technique in the field of power system, and eventually bring some positive impacts to the real-world bulk grid resilient and economic control and operation

Offtake Strategy Design for Wind Energy Projects under Uncertainty

Energy use from wind, solar, and other renewable sources is a public policy at the federal and state levels to address environment, energy, and sustainability concerns. As the cost of renewable energy is still relatively high compared to fossil fuels, it remains a critical challenge to make renewable energy cost competitive, without relying on public subsidies. During recent years, much advance has been made in our understanding of technology innovations and cost structure optimization of renewable energy. A knowledge gap exists on the other side of the equation - revenue generation. Considering the complexity and stochastic nature of renewable energy projects, there is great potential to optimize the revenue generation mechanisms in a systematic fashion for improved profitability and growth. This dissertation examines two primary revenue generation mechanisms, or offtake strategies, used in wind energy development projects in the U.S. While a short-term offtake strategy allows project developers to benefit from price volatility in the wholesale spot market for profit maximization, a long-term offtake strategy minimizes the market risk exposure through a long-term Power Purchase Agreement (PPA). With Conditional Value-at-Risk (CVaR) introduced as a risk measure, this dissertation first develops two stochastic programming models for optimizing offtake designs under short and long-term strategies respectively. Furthermore, this study also proposes a hybrid offtake strategy that combines both short and long-term strategies. The two-level stochastic model demonstrates the merit of the hybrid strategy, i.e. obtaining the maximized profit while maintaining the flexibility of balancing and hedging against market and resource risks efficiently. The Cape Wind project in Massachusetts has been used as an example to demonstrate the model validity and potential applications in optimizing its revenue streams. The analysis shows valuable implications on the optimal design of renewable energy project development in regard to offtake arrangements

A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

During the two-year project period, the consortium members have developed control algorithms for enhancing the reliability of wind turbine components. The consortium members have developed advanced operation and planning tools for accommodating the high penetration of variable wind energy. The consortium members have developed extensive education and research programs for educating the stakeholders on critical issues related to the wind energy research and development. In summary, The Consortium procured one utility-grade wind unit and two small wind units. Specifically, the Consortium procured a 1.5MW GE wind unit by working with the world leading wind energy developer, Invenergy, which is headquartered in Chicago, in September 2010. The Consortium also installed advanced instrumentation on the turbine and performed relevant turbine reliability studies. The site for the wind unit is InvenergyÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs Grand Ridge wind farmin Illinois. The Consortium, by working with Viryd Technologies, installed an 8kW Viryd wind unit (the Lab Unit) at an engineering lab at IIT in September 2010 and an 8kW Viryd wind unit (the Field Unit) at the Stuart Field on IITÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs main campus in July 2011, and performed relevant turbine reliability studies. The operation of the Field Unit is also monitored by the Phasor Measurement Unit (PMU) in the nearby Stuart Building. The Consortium commemorated the installations at the July 20, 2011 ribbon-cutting ceremony. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on turbine reliability included (1) Predictive Analytics to Improve Wind Turbine Reliability; (2) Improve Wind Turbine Power Output and Reduce Dynamic Stress Loading Through Advanced Wind Sensing Technology; (3) Use High Magnetic Density Turbine Generator as Non-rare Earth Power Dense Alternative; (4) Survivable Operation of Three Phase AC Drives in Wind Generator Systems; (5) Localization of Wind Turbine Noise Sources Using a Compact Microphone Array; (6) Wind Turbine Acoustics - Numerical Studies; and (7) Performance of Wind Turbines in Rainy Conditions. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on wind integration included (1) Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection; (2) Large-scale Analysis of 2018 Wind Energy Integration in the Eastern U.S. Interconnection; (3) Integration of Non-dispatchable Resources in Electricity Markets; (4) Integration of Wind Unit with Microgrid. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs education and outreach activities on wind energy included (1) Wind Energy Training Facility Development; (2) Wind Energy Course Development; (3) Wind Energy Outreach

Mathematical framework for designing energy matching and trading within green building neighbourhood system

Nowadays, energy efficiency, energy matching and trading, power production based on renewable energyresources, improving reliability, increasing power quality and other concepts are providing the most important topics in the power systems analysis especially in green building in the neighbourhood systems (GBNS). To do so, the need to obtain the optimal and economical dispatch of energy matching and trading should be expressed at the same time. Although, there are some solutions in literature but there is still a lack of mathematical framework for energy matching and trading in GBNS. In this dissertation, a mathematical framework is developed with the aim of supporting an optimal energy matching and trading within a GBNS.This aim will be achieved through several optimization algorithms based on heuristic and realistic optimization techniques. The appearance of new methods based on optimization algorithms and the challenges of managing a system contain different type of energy resources was also replicating the challenges encountered in this thesis. As a result, these methods are needed to be applied in such a way to achieve maximum efficiency,enhance the economic dispatch as well as to provide the best performance in GBNS. In order to validate theproposed framework, several case studies are simulated in this thesis and optimized based on various optimization algorithms. The better performances of the proposed algorithms are shown in comparison with the realistic optimization algorithms, and its effectiveness is validated over several GBs. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition. The obtained results clearly show that the proposed framework is effective in achieving optimal dispatch of generation resources in systems with multiple GBs and minimizing the market clearing price for the consumers and providing the better utilization of renewable energy sources

A Mixed Integer Linear Unit Commitment and Economic Dispatch Model for Thermo-Electric and Variable Renewable Energy Generators With Compressed Air Energy Storage

The objective of this PhD thesis is to create a Unit Commitment and Economic Dispatch (UCED) modelling tool that can used to simulate the deterministic performance of a power system with thermal and renewable generators and energy storage technologies. The model was formulated using mixed integer programing (MIP) on GAMS interface. A robust commercial solver by IBM (CPLEX) is used as solver. Emphasis on the development of the tool has been given on the following aspects. a) Technical impacts of Variable Renewable Energy (VRE) integration. The UCED model developed in this thesis is a high resolution short-term dispatch model. It captures the variability of VRE power on the intra-hour level. In addition the model considers a large number of important real world, system, unit and policy constraints. Detailed representation of a power system allows for a realistic estimation of maximum penetration levels of VRE and the related technical impacts like cycling of generators (part-loading and number of start-ups). b) CO2 emissions. High levels of VRE penetration can potentially increase consumption of fuel in thermal units per unit of electricity produced due to increased thermal cycling. The dispatch of units in the UCED model is based on minimizing system wide operational costs the most important of those being fuel, start-up costs and the cost of carbon. Fuel consumption is calculated using technical data from Input/Output curves of individual generators. The start-up cost is calculated based on times the generator units have been off and the energy requirement to bring the unit back to hot state. Thus dynamic changes on fuel consumption can be captured and reported. c) Technical solutions to facilitate VRE integration. VRE penetration can be facilitated if appropriate solutions are implemented. Energy storage is an effective way to reduce the impact of RE variability. The UCED model includes an integrated Mixed Integer Linear (MILP) compressed air energy storage (CAES) simulation sub-model. Unlike existing CAES models, the new “Thermo-Economic” (TE) CAES model developed in this thesis uses technical data from major CAES manufacturers to model the dynamic effect of cavern pressure on both the compression and expansion sides during CAES operation. More specifically the TE model takes into account that a) a compressor discharges at a pressure equal to the back-pressure developed in the cavern at each moment, b) the speed of charging can be regulated through inlet guide vanes; higher charging speed can take place at the expense of additional power consumption, c) the maximum power output during expansion can be limited by the levels of cavern pressure; there is a threshold pressure level below which the maximum output decreases linearly with pressure. Since it uses actual power curves to simulate CAES operation, the TE model can be assumed to be more accurate than conventional Fixed Parameter (FP) models that don’t model dynamic effects of cavern pressure on CAES operation. The TE model in this thesis is compared with conventional FP models using historical market prices from the Irish electricity market. The comparison was based on the ability of a CAES unit to arbitrage energy for making profit in the Irish electricity market. More specifically a “Base” scenario was created that included the operation of a 270MW CAES unit with technical characteristics obtained from a major CAES manufacturer and assumed discharge time of 13hr. Various sensitivities on discharge time, natural gas prices and system marginal prices (SMPs) were modeled. An additional scenario was created to show the benefit on CAES profitability if the unit participated in both the energy and ancillary services markets. All scenarios were modeled using both the TE and FP CAES models. The results showed that the most realistic TE model returns around 15% less profitability across more scenarios. The reduction in profitability grows to around 30% when the cavern volume (discharge time) is reduced to half (6 hours). The latter is related to the sensitivity of the TE model on cavern pressure that is being built faster when the volume is reduced. A CAES unit won’t get a positive net present value (NPV) in Ireland under any scenario unless SMPs are greatly increased. Thus, it was shown that that existing FP CAES models overestimate CAES profitability. More accurate models need to be used to estimate CAES profitability in deregulated markets. Additionally, it might deem necessary to create additional markets for energy storage units and increase the possible revenue sources and magnitude to facilitate an increase of storage capacity worldwide. The second step of analysis involved the integration of the CAES and UCED models. The UCED model developed in this thesis was validated and applied using data from the Irish grid, a power system with more than 50 thermal generators. A vast of existent data was used to create a mathematical model of the Irish system. Such data include technical specifications and variables of thermal generators, maintenance schedules and historical solar, wind and demand data. The validation exercise was deemed successful since the UCED model simulated utilization factors of 45 out of 52 generators with an absolute difference between modeled and actual results on utilization factors of less than 6% (the absolute differences are called Delta in this thesis). In addition the results of validation exercise were compared with the results of a similar exercise where PLEXOS was the modelling tool and it was found that the results of the two models were similar for the vast majority of generators. More specifically, the PLEXOS model results showed higher deltas for the coal-fired generators compared to the UCED model. On the other hand the UCED model, reported higher delta values for peat-fired generators. The results of the PLEXOS model were slightly better for the gas-fired generators while both models reported deltas nearly zero for all oil and distillate-fired generators. Finally the model was applied to study the benefits of energy storage in Ireland in 2020 when wind penetration is expected to reach 37% of total demand. The analysis involved the development of two groups of 3 scenarios each. In the first group the main scenario also called the “Reference” was used to simulate the short-term unit (30 min step) commitment within the Irish system without storage. The results of the reference scenario were compared with two additional scenarios that assumed the existence of one 270MW CAES unit in Northern Ireland by 2020 (again the first scenario involved the TE and the second the FP CAES model). The results showed –when using the TE model- that the inclusion of one 270MW CAES unit in AI can help reduce wind curtailment by 88GWh, CO2 emissions by 150,000 tonnes and system costs by € 6 million per year. If an FP model had been used instead the reductions would be: wind curtailment by 108GWh, CO2 emissions by 270,000 tonnes and annual system costs by €13 million. Two main conclusions can be obtained from the specific set of results. The first conclusion is that storage units have a financial benefit over the whole system. Thus, when a CAES unit operates to minimize the costs of the whole system can incur substantially more benefits compared to if the CAES unit operated to maximize the individual unit’s profits as in the case presented earlier. The benefits of storage over the whole system should be accounted to make policy decisions and create incentives for investors to increase energy storage capacity in national grids. The second important conclusion is that existing CAES FP models overestimate the ability of a CAES unit to facilitate VRE penetration. More accurate TE models should be used to assess a unit’s capability to increase system flexibility. A second group of scenarios was created to simulate the benefit of CAES at even higher VRE penetration levels. In the second group the “Reference” scenario again, assumed no storage however, wind production was increased by 25%. Again the “Reference” was compared with two additional scenarios that assumed integration of 3x270MW=810MW of storage capacity in AI (one scenario used the TE model and the other the FP). The results for the TE model show that each of the 3 CAES units reduces wind curtailment by 188,000MWh, total system costs by €29 million and CO2 emissions by 180,000 tonnes. The same reductions for the FP model are 217,000MWh of wind curtailment, €25.6 million on total system costs and 180,000 tonnes of CO2. Thus, the results of the second group of scenarios show that as the installed capacity of both CAES and wind increases in Ireland a) the system-wide benefits of CAES increase and b) the differences on results between the TE and FP models become much smaller