In the current era of deregulated electricity markets, the power distribution systems have attained a very important and crucial role in the industry. A distribution company (referred to as a disco) plays an active and effective role in electricity markets, and can positively impact the market efficiency and make it more reliable, secure and beneficial to customers. Therefore, operation and planning issues of discos in such electricity market environment requires extensive analysis and research in order to improve their operational strategies both in the short-term and long-term. A generic operations framework for a disco operating in a competitive electricity market environment is presented in the thesis. The operations framework is a two-stage hierarchical model in which the first stage deals with disco’s activities in the day-ahead stage, the Day Ahead Operations Model (DAOM). The second stage deals with disco’s activities in real-time and is termed Real-Time Operations Model (RTOM). The DAOM determines the disco’s operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco’s actions and decision-making in this context. The thesis presents a new paradigm for distribution system operation taking into account the presence of DG sources and their goodness factors. The proposed concept of goodness factor of DG units is based on the computation of the incremental contribution of a DG unit to distribution system losses. The incremental contributions of a DG unit to active and reactive power losses in the distribution system are termed as the active / reactive Incremental Loss Indices (ILI). The goodness factors are integrated directly into the distribution system operations model. This model seeks to minimize the disco’s energy costs in the short-term taking into account the contribution (goodness factor) of each DG unit. The analysis was carried out considering an 18-bus distribution network, considering two different ownership structures of DG units, and a 69-bus distribution system considering specific characteristics of wind-DG units. The concept of goodness factors is further extended to determine a new set of goodness factors pertaining to a DG’s impact on feeder unloading by virtue of its power injection. A novel long-term planning model has been developed for the disco that considers investments in DG capacity, distribution system feeder addition / expansion and substation transformers capacity addition. The model includes the new set of goodness factors pertaining to both loss reduction and feeder unloading and arrives at an optimal set of new expansion plan, with specified locations, and year of commissioning. The work clearly demonstrates the effectiveness and contribution of DG units in distribution systems both in the short-term and long-term framework.

This thesis presents a multi-stage linear stochastic mixed integer programming (SMIP) model for planning power generation in a pool-type day-ahead electricity market. The model integrates a reserve demand curve and shares most of the features of a stochastic unit commitment (UC) problem, which is known to be NP-hard. We capture the stochastic nature of the problem through scenarios, resulting in a large-scale mixed integer programming (MIP) problem that is computationally challenging to solve. Given that an independent system operator (ISO) has to solve such a problem within a time requirement of an hour or so, in order to release operating schedules for the next day real-time market, the problem has to be solved efficiently. For that purpose, we use some approximations to maintain the linearity of the model, parsimoniously select a subset of scenarios, and invoke realistic assumptions to keep the size of the problem reasonable. Even with these measures, realistic-size SMIP models with binary variables in each stage are still hard to solve with exact methods. We, therefore, propose a scenario-rolling heuristic to solve the SMIP problem. In each iteration, the heuristic solves a subset of the scenarios, and uses part of the obtained solution to solve another group in the subsequent iterations until all scenarios are solved. Two numerical examples are provided to test the performance of the scenario-rolling heuristic, and to highlight the difference between the operative schedules of a deterministic model and the SMIP model. Motivated by previous studies on pricing MIP problems and their applications to pricing electric power, we investigate pricing issues and compensation schemes using MIP formulations in the second part of the thesis. We show that some ideas from the literature can be applied to pricing energy/reserves for a relatively realistic model with binary variables, but some are found to be impractical in the real world. We propose two compensation schemes based on the SMIP that can be easily implemented in practice. We show that the compensation schemes with make-whole payments ensure that generators can have non-negative profits. We also prove that under some assumptions, one of the compensation schemes has the interesting theoretical property of minimizing the variance of the profit of generators to zero. Theoretical and numerical results of these compensation schemes are presented and discussed.

In deregulated electricity markets, generating companies submit energy bids which are derived from a self-schedule. In this thesis, we propose an improved semidefinite programming-based model for the self-scheduling problem. The model provides the profit-maximizing generation quantities of a single generator over a multi-period horizon and accounts for uncertainty in prices using robust optimization. Within this robust framework, the price information is represented analytically as an ellipsoid. The risk-adversity of the decision maker is taken into account by a scaling parameter. Hence, the focus of the model is directed toward the trade-off between profit and risk. The bounds obtained by the proposed approach are shown to be significantly better than those obtained by a mean-variance approach from the literature. We then apply the proposed model within a branch-and-bound algorithm to improve the quality of the solutions. The resulting solutions are also compared with the mean-variance approach, which is formulated as a mixed-integer quadratic programming problem. The results indicate that the proposed approach produces solutions which are closer to integrality and have lower relative error than the mean-variance approach.

This dissertation proposes new methods to improve the efficiency of electricity markets with respect to market monitoring and reserve allocation. We first present new approaches to monitor the level of competition in electricity markets, a critical task for helping the markets function smoothly. The proposed approaches are based on economic principles and a faithful representation of transmission constraints. The effectiveness of the new approaches is demonstrated by examples based on medium- and large-scale electric power systems. We then propose a new system-operation model using stochastic optimization to systematically allocate reserves under uncertainty. This model aims to overcome the difficulties in both system and market operations caused by the integration of wind power, which results in a higher degree of supply uncertainty. The numerical examples suggest that the proposed model significantly lower the operation costs, especially under high levels of wind penetration. / text

This cumulative doctoral thesis comprises three essays in which "Data Envelopment Analysis"(DEA), an instructive and flexible analytic tool with origins in operations research, is utilized to help clarify three crucial issues that arise when subjecting network industries to price-cap regulation. These issues are: First, the relationship of price-cap regulation with investment, second, the relationship of price-cap regulation with quality and third, the correct cost of capital within price-cap regulation. Without loss of generality, the investigation is focused on the case of electricity. (author's abstract)

Over the last 20 years, electricity markets around the world have gradually been deregulated, creating wholesale markets in which generating companies compete for the right to supply electricity, through an offering system. This thesis considers the optimisation of the offering process from the perspective of an individual generator, subject to intertemporal constraints including fuel limitations, correlated rest-of-market behaviour patterns and unit operational decisions. Contributions from the thesis include a Pre-Processing scheme that results in considerable computational benefits for a two-level Dynamic Programming method, in addition to the development of a new process that combines the techniques of Decision Analysis and Dynamic Programming.

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in recent year has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This thesis investigates the impact of smart grid technologies on the integration of wind power into the power system. A smart grid power system model is developed and validated by comparison with a real-life smart grid experiment: the Olympic Peninsula Demonstration Experiment. The smart grid system model is then expanded to include 1000 houses and a generic generation mix of nuclear, hydro, coal, gas and oil based generators. The effect of super-imposing varying levels of wind penetration are then investigated in conjunction with a market model whereby suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. It is also found that the degree to which Greenhouse Gas (GHG) emissions can be mitigated is highly dependent on the generation mix. A displacement of natural gas based generation during peak demand can for instance lead to an increase in GHG emissions. Of practical significance to power system operators, the simulations also demonstrate that Demand Response (DR) can reduce generator cycling and improve generator efficiency, thus potentially lowering GHG emissions while also reducing wear and tear on generating equipment. / Graduate

We examine the relationship between German wind and solar electricity and French spot price volatility. Using hourly data, we find that French imports from Germany driven by German wind and solar electricity sometimes decrease, sometimes increase the volatility of French spot prices. These two opposing effects depend on the shape of the French supply function and on the French demand. We, therefore, estimate different coefficients for imports depending on different demand levels. We acknowledge the endogeneity problem in identifying these effects and employ instrumental variable techniques to circumvent this problem. Our results show the urgent need for further coordination of national energy policies in order to reduce the potential for negative spill over effects of nationally driven energy policies in neighbouring countries as European electricity markets are becoming more integrated. / Series: Department of Economics Working Paper Series

This report aims to make a comparison between the Swedish and Spanish electricity market, the design of new improvements that could achieve a better operation for both markets as well as the price forecasting for both spot markets. These enhancements are oriented to decrease electricity prices, energy use and the system CO2 emissions. Also, the main organizations of the market and their roles has been characterized, clarifying the functions of the Market Operator and the System Operator. In addition, the different markets, the trading products and the price formation have been explained and the picture of the market structure has been achieved with enough depth. Moreover, some of the most used methods in Time Series Analysis has been enumerated to understand which techniques are needed for forecast the electricity prices and the methodology used (Box-Jenkins Method) has been explained in detail. Later, all these methods have been implemented in an own code developed in Python 3.6 (TSAFTools .py) with the help of different statistics libraries mentioned during the method chapter. On the other hand, the description of the market situation has been carried out for both countries. Power installed capacity, electricity generation, average prices, main renewable technologies and policies to increase the renewable energy share has been analysed and corresponding described. Then, to estimate the market’s future spot electricity prices, ARIMA models have been selected to analyse the evolution of the day-ahead price using the TSAFTools.py. The final models show a proper performance in the two markets, especially in the Nordpool, achieving an RMSE: 37.68 and MAPE: 7.75 for the year in 2017 in Nordpool and a RMSE: 270.08 and MAPE: 20.24 in OMIE for 2017. Nordpool spot prices from 2015 to 2016 has been analysed too but obtaining a result not as good as the year 2017 with an RMSE: 49.01 and MAPE: 21.42. After this analysis, the strengths and weaknesses of both markets are presented and the main problems of the Spanish electricity system (power overcapacity, fuel dependency, non-cost-efficient renewable energies policies, lack of interconnexion capacity etc.) and the Swedish electricity system (dependency for nuclear power, uncertainty for solar electricity Generation) are presented. Finally, due to the quick development of the energy sector in the last years and the concern of the European Committee to reach a new design for the electricity market, different kinds of recommendations for the future have been considered.

The electricity transmission and distribution (T&D) grid is undergoing a paradigm shift as renewable generation explodes while flexible, storage-like loads are being massively adopted. We address the intermittency and volatility issues of renewable resources in connection with spatiotemporal distribution location-specific marginal-cost-based prices (DLMPs) that guide flexible loads to utilize their significant degrees of freedom for the purpose of providing valuable storage-like services to the grid including demand response, energy charge/discharge arbitrage and regulation reserve services. Dynamic DLMPs can induce socially optimal energy and reserve schedules to be adopted by flexible load. To this end, existing transmission wholesale markets must be extended to include distribution network connected participants. Since the inclusion of the complex preferences of many flexible loads renders familiar centralized transmission market designs intractable, we propose and investigate tractable decentralized market designs with Electric Vehicle (EV) battery charging selected as the representative flexible load. We address the equilibrium existence, uniqueness, and efficiency issues that arise with decentralized market designs, using game theory techniques. We investigate various multi-hour and multi-commodity (energy and reserves) market designs including EV self-scheduling under distribution network information aware/unaware conditions, and single or multiple load aggregator(s) scheduling groups of EVs. We investigate the role of network related information in enabling partially price anticipating EVs to acquire market power and self-schedule to achieve individual benefits at the expense of social welfare. Our contribution is the proof of existence and uniqueness of decentralized market equilibria, as well as analytical and numerical comparative analysis. Secondly, we depart from the usual ideal battery assumption, employing instead a realistic two bucket model. We then develop a novel Markovian Decision Process (MDP) application to estimate the regulation tracking cost incurred over an hour by an EV charger employing an optimal controller to respond to the regulation signal which is reset every two seconds by the system operator. The hourly tracking error increases when the EV promises higher regulation reserves while at the same time demanding an achievable albeit high average charging rate. We solve the MDP repeatedly, in fact off line, to capture the impact of the average charging rate and the regulation reserves promised at the beginning of an hour to the resulting hourly regulation tracking error. We then estimate a convex closed form relationship mapping hourly charging rate and regulation reserve offerings to the expected hourly tracking error cost. These convex tracking cost functions provide crucial input to the day ahead hourly energy bids and regulation reserve offers made by individual EVs to the Day Ahead market in response to spatiotemporal DLMPs.