CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

Market coupling in the power markets

The thesis analyses the European Unions’ effort to create an integrated pan-European electricity market based on “market coupling” as the proposed allocation mechanism for interconnector transfer capacity. Thus, the thesis’ main focus is if market coupling leads to a price convergence in interlinked markets and how it affects the behavior of electricity price data. The applied research methods are a qualitative, structured literature review and a quantitative analysis of electricity price data. The quantitative analysis relies on descriptive statistics of absolute price differentials and on a Cointegration analysis according to Engle & Granger (1987)’s two step approach. Main findings are that implicit auction mechanisms such as market coupling are more efficient than explicit auctions. Especially the method of price coupling leads to a price convergence in involved markets, to social welfare gains and reduces market power of producers, as shown on the example of the TLC market coupling. The market coupling initiative between Germany and Denmark, on the other hand, is evaluated as less successful and illustrates the complexity and difficulties of implementing market coupling initiatives. The cointegration analysis shows that the time series were already before the coupling date cointegrated, but the statistical significance increased. The thesis suggests that market coupling leads to a price convergence of involved markets and thus functions as method to create a single, integrated European electricity market.

On the efficiency of the Argentinean electricity wholesale market

This paper discusses two key aspects regarding the efficiency of the Argentinean Electricity Market. Using hourly data on prices, marginal costs, and operational status of generators, it will be argued that, unlike the former British and Californian electricity spot markets, this market is not subject to the conventional forms of exercise of market power by generators. We then use Chao's (1983) model of optimal configuation of electricity supply to evaluate the social desirability of the change in the supply pattern of the Argentinean electricity industry, which took place throughout the last ten years.

Designing Electricity Auctions in the Presence of Transmission Constraints

This paper analyzes the effect that different designs in the access to fnancial transmission rights has on spot electricity auctions. In particular, I characterize the equilibrium in the spot electricity market when financial transmission rights are assigned to the grid operator and when financial transmission rights are assigned to the firm that submits the lowest bid in the spot electricity auction. When financial transmission rights are assigned to the grid operator, my model, in contrast with the models available in the literature, works out the equilibrium for any transmission capacity. Moreover, I have found that an increase in transmission capacity not only increases competition between markets but also within a single market. When financial transmission rights are assigned to the firm that submits the lowest bid in the spot electricity auction, firms compete not only for electricity demand, but also for transmission rights and the arbitrage profits derived from its hold. I have found that introduce competition for transmission rights reduces competition in spot electricity auctions.

The first renewable forward market mechanisms in the Iberian Electricity Market

A description of the first renewable forward market mechanisms in the Iberian Electricity Market is provided. A contract for difference mechanism is available in Spain since March 2011between the last resort suppliers and the special regime (renewables and cogeneration) settling the price differences between the equilibrium price of the forward regulated auctions for the last resort supply and the spot price of the corresponding delivery period. Regulated auctions of baseload futures of the Portuguese zone in which the Portuguese last resort supplier sells the special regime production exist since December 2011. The experience gained from renewables auctions in Latin America could be used in the Iberian Electricity market, complementing these first market mechanisms. Introduction of renewable auctions at least for the most mature technologies (i.e. wind) in Spain and Portugal providing a fair price for the renewable generation will be of utmost importance in the short term to diminish the tariff deficit caused by the massive deployment of the feed-in-tariff scheme. Liquidity in the forward markets will also increase as a result of the entry of renewable generation companies intending to maximize their profits due to gradual suppression of feed in tariff schemes.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al(2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models(according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combi nation of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to Decem ber 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

Outlook for Electricity and Renewable Energy in Southern and Eastern Mediterranean Countries. MEDPRO Technical Report No. 16/October 2012

The aim of this report is to elaborate the MEDPRO Energy Reference Scenario for electricity demand and power generation (by energy source) in the southern and eastern part of the Mediterranean (MED- 11 countries) up to 2030. The report assesses  the prospects for the implementation of renewable energy in the MED-11 countries over the next decades. The development of renewable energy is a cornerstone of the MED-11 countries’ efforts to improve security of supply and reduce CO2 emissions;  the prospects for regional renewable-energy plans (the Mediterranean Solar Plan, DESERTEC and Medgrid); and  the development of electricity interconnections in MED-11 countries and the possible integration of Mediterranean electricity and renewable markets (both south–south and south–north).

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

A comparison of integrated biomass to electricity systems

This thesis presents a comparison of integrated biomass to electricity systems on the basis of their efficiency, capital cost and electricity production cost. Four systems are evaluated: combustion to raise steam for a steam cycle; atmospheric gasification to produce fuel gas for a dual fuel diesel engine; pressurised gasification to produce fuel gas for a gas turbine combined cycle; and fast pyrolysis to produce pyrolysis liquid for a dual fuel diesel engine. The feedstock in all cases is wood in chipped form. This is the first time that all three thermochemical conversion technologies have been compared in a single, consistent evaluation.The systems have been modelled from the transportation of the wood chips through pretreatment, thermochemical conversion and electricity generation. Equipment requirements during pretreatment are comprehensively modelled and include reception, storage, drying and communication. The de-coupling of the fast pyrolysis system is examined, where the fast pyrolysis and engine stages are carried out at separate locations. Relationships are also included to allow learning effects to be studied. The modelling is achieved through the use of multiple spreadsheets where each spreadsheet models part of the system in isolation and the spreadsheets are combined to give the cost and performance of a whole system.The use of the models has shown that on current costs the combustion system remains the most cost-effective generating route, despite its low efficiency. The novel systems only produce lower cost electricity if learning effects are included, implying that some sort of subsidy will be required during the early development of the gasification and fast pyrolysis systems to make them competitive with the established combustion approach. The use of decoupling in fast pyrolysis systems is a useful way of reducing system costs if electricity is required at several sites because• a single pyrolysis site can be used to supply all the generators, offering economies of scale at the conversion step. Overall, costs are much higher than conventional electricity generating costs for fossil fuels, due mainly to the small scales used. Biomass to electricity opportunities remain restricted to niche markets where electricity prices are high or feed costs are very low. It is highly recommended that further work examines possibilities for combined beat and power which is suitable for small scale systems and could increase revenues that could reduce electricity prices.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Essays on market design and regulation in electricity systems

This thesis discusses market design and regulation in electricity systems, focusing on the information exchange of the regulated grid firm and the generation firms as well as the regulation of the grid firm. In the first chapter, an economic framework is developed to consistently analyze different market designs and the information exchange between the grid firm and the generation firms. Perfect competition between the generation firms and perfect regulation of the grid firm is assumed. A numerical algorithm is developed and its feasibility demonstrated on a large-scale problem. The effects of different market designs for the Central Western European (CWE) region until 2030 are analyzed. In the second chapter, the consequences of restricted grid expansion within the current market design in the CWE region until 2030 are analyzed. In the third chapter the assumption of efficient markets is modified. The focus of the analysis is then, whether and how inefficiencies in information availability and processing affect different market designs. For different parameter settings, nodal and zonal pricing are compared regarding their welfare in the spot and forward market. In the fourth chapter, information asymmetries between the regulator and the regulated firm are analyzed. The optimal regulatory strategy for a firm, providing one output with two substitutable inputs, is defined. Thereby, one input and the absolute quantity of inputs is not observable for the regulator. The result is then compared to current regulatory approaches.

Coordinated Scheduling of Wind-Thermal Gencos in Day-Ahead Markets

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modeled by variable costs, start-up costs and technical operating constraints, such as: ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, aiming to maximize the expected profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.