Handbook of Networks in Power Systems I

Starting from the nineties of the last century, competition has been introduced in the electricity industry around the world, as a tool to increase market efficiency and decrease prices. Electricity is a commodity that needs to be traded over a physical network with strict physical and operational constraints that cannot be found in other commodity markets. Present electricity markets may be better described in terms of oligopoly than of perfect competition from which they may be rather far. In an oligopoly market, the producer is a market player that shows strategic behavior, submitting offers higher than the marginal costs, as they under perfect competition, with the aim to maximize its individual surpluses. The market clearing price, quantities and the market efficiency depending on the strategic interactions among producers must be taken into account in modeling competitive electricity markets. The network constraints provide very specific opportunities of exercising strategic behaviors to the market participants. Game theory provides a conceptual framework and analytical tool to model such a context. The modeling of electricity markets will be presented by discussing the traditional Game Theory models, such as bertrand, cournot, conjecture supply function, supply function equilibrium, adapted to be able to capture, in determining the Nash equilibrium, the network structure of the system in which the market is implemented. A formalized representation and a comparison of some of the most common game theory models will be provided with some conceptual examples. In addition, some newly proposed approaches for strategic bidding modeling based on the complex systems techniques such as Multi Agent systems and Complex Networks will be mentioned and some related references provided.

We review some mathematical programming models that capture the optimal bidding problem that power producers face in day-ahead electricity auction markets. The models consider both price-taking and non-price taking assumptions. The models include linear and non-linear integer programming models, mathematical programs with equilibrium constraints, and stochastic programming models with recourse. Models are emphasized where the producer must self-schedule units and therefore must integrate optimal bidding with unit commitment decisions. We classify models according to whether competition from competing producers is directly incorporated in the model.

In restructured electricity markets, generators and other market participants submit bids to the system operator, who dispatches power while satisfying the system constraints. Dispatch establishes the market clearing price for electricity at each node of the network. In recent years, power market participants have begun competing in electricity-related markets for financial instruments such as financial transmission rights (FTRs) and CO ₂ allowances. Benefits derived from these markets depend largely on the electricity dispatch. For example, payments received by FTR holders are determined by the differential market clearing price between the nodes; CO ₂ allowances needed by the generators depend on the amount of fossil fuel-based electricity dispatched in the network. Hence, the participants must develop bidding strategies that maximize their joint profits from electricity and other related markets. This chapter, presents a multi-tier game theoretic framework that can be used to develop joint bidding strategies. In the electricity market, we focus on day-ahead and spot market bidding. Though there are many market participants (generators, loads, and third parties), the framework presented in this chapter caters directly to the needs of the generators. The multi-player matrix games underlying the framework are solved using an approach that incorporates a reinforcement learning algorithm. Application of the framework is exemplified via three example problems.

In this chapter, short-term electricity price modeling and forecasting in competitive electricity markets is presented. Dominant characteristics of short-term electricity prices such as, seasonality, non-stationarity, spikes and volatility are discussed and numerical examples from real-life markets are presented. A review of the existing literature on short-term electricity price forecasting is also provided, followed by an overview of the process of building data-driven models for electricity prices. Furthermore, some popular time series models for electricity market price modeling and forecasting, such as ARIMA models, are discussed. A case study is also presented in which Ontario electricity market prices are modeled and 24-h-ahead forecast are generated.

Electricity is a fundamental good for society. The price at which it is sold as a commodity influences all levels of economic activity and determines the profits and benefits that generators and consumers reap from participating in the electricity markets. Forecasting the electricity prices at different time-frames, namely in the short-run (daily), medium-term (seasons) or long-term (years), is of foremost importance for all industry stakeholders for cash flow analysis, capital budgeting and financial procurement as well as regulatory rule-making and integrated resource planning, among others. On the other hand, the process of price formation in competitive electricity markets is unique in terms of the different factors that come into play in the settlement process. These factors, which may be endogenous or exogenous to the market, bring about uncertainty and volatility to the electricity prices. This uncertainty hinders the forecast user’s ability to estimate the prices with accuracy at the different time-frames. This chapter explores the different reasons why forecasting electricity prices is necessary in electricity markets, the most widely used methodologies for short-term electricity price forecasting and their fundamental common limitations. This analysis is carried out using actual electricity price datasets.

Electricity markets are composed of different agents that make their offers to sell and/or buy energy. These agents need forecasting tools to have an accurate prediction of the prices that they will face either in the day-ahead or long-term time spans. This work presents the ECOnometrics TOOLbox (ECOTOOL), a new MATLAB forecasting toolbox that embodies several tools for identification, validation and forecasting models based on time series analysis, among them, ARIMA, Exponential Smoothing, Unobserved Components, ARX, ARMAX, Transfer Function, Dynamic Regression and Distributed Lag models. The toolbox is presented in all its potentiality and several real case studies, both on the short and medium term, are shown to illustrate its applicability.

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in sophisticated tools very helpful under this context.

Some simulation tools have already been developed, some of them very interesting. However, at the present state it is important to go a step forward in Electricity Markets simulators as this is crucial for facing changes in Power Systems. This paper explains the context and needs of electricity market simulation, describing the most important characteristics of available simulators. We present our work concerning MASCEM simulator, presenting its features as well as the improvements being made to accomplish the change and challenging reality of Electricity Markets.

The paper addresses the idea of reliability differentiation for peer electricity customers willing to choose a standardized reliability level of electricity supply for the respective tariff.

The authors suggest the reliability differentiation concept based on standardized reliability levels (categories). As a partial prototype, the existing 3-grade reliability differentiation system in Lithuania is referred to. According to the concept, customers with higher reliability category pay a higher distribution reliability tariff, in proportion to the incurred grid operation, management and amortization cost.

The operator provides a contracted higher category mainly through the parallel supply path in the grid, i.e. by the switch-over of customer’s load to the 2nd independent supply point after the failure of electrical path from the 1st independent supply point. Accordingly, the supply notion is split to major, joint and reserving supply where major supply for a customer is provided in normal operation situations (i.e. from the 1st independent supply point), reserving supply – in unusual situations (from 2nd independent supply point) while joint supply denotes situation when the same electrical path serves as both a major supply route for one customer and a reserve supply route for another.

The suggested concept is supported by a new mathematical reliability pricing model for 2-grade reliability system. The model is cost-reflective and applicable to the distribution grid area controlled by one operator. Its rationale is fair allocation of grid cost between customer groups with different reliability categories, and subsequent derivation of distribution tariffs for reliability categories. The grid cost is split to the components related to four types of grid equipment – distribution transformers, medium voltage lines, local transformers and low voltage lines. These cost components are allocated between customer classes with different reliability categories and connection-to-grid voltages. An allocation criterion is the scope of usage of grid equipment type.

The applicability of the presented model is illustrated by numerical setup for the sample grid.

The paper formulates an electricity delivery scheduling problem in accordance with the bilateral contracts in the competitive wholesale market. Bilateral forward contracts are generally used in electricity markets to stabilize prices and hedge risks of electricity shortage. A contract party is able to draw electricity from the contract and resell it to the day-ahead wholesale and retail markets. Contract parties schedule electricity deliveries over contract period to get the highest profit. The problem solution results in determination of a contract price acceptable for both parties. Normally the contract parties are interested in different delivery strategies during the contract period. A compromise in determination of the delivery strategy implies obtaining the equality of relative concessions from supplier and buyer.

Conclusion of bilateral contracts bears certain risks due to price and demand uncertainty. Both contract parties forecast price levels in the spot market and electricity demand. Both parties estimate expected profits (or losses) from caring out the contract and participation in the spot market. It is important to adjust the contract in time.

The paper formulates optimization problems for contract scheduling. A numerical example demonstrates the efficiency of the algorithm.

The sequence of actions to be performed for contract correction is considered. The statements of optimization problems are given for decision making on contract correction and cancellation. The statements take into account financial compensation to another party in the case of prescheduled contract correction or cancellation. A numerical example illustrates applicability of the proposed procedures for decision making.

We review supply function equilibrium models and their predictions on market outcomes in the wholesale electricity auctions. We discuss how observable market characteristics such as capacity constraints, number of power suppliers, load distribution and auction format affect the behavior of suppliers and performance of the market. We specifically focus on the possible market power exerted by pivotal suppliers and the comparison between discriminatory and uniform-price auctions. We also describe capacity investment behavior of electricity producers in the restructured industry.

Demand Response (DR) programs, which aim to reduce electricity consumption in times of high energy cost or network constraints by allowing customers to respond to price or quantity signals, are becoming very popular in many electricity systems, frequently associated to smart-grid developments. These programs could entail significant benefits for power systems and the society as a whole. Assessing the magnitude of these benefits is crucial to determine their convenience, especially when there are non negligible costs associated to their implementation (if advanced metering infrastructure or control technologies are needed). Quantifying DR benefits requires first to estimate the changes in demand patterns that can potentially be achieved and then to evaluate the effects of those changes on the complex behavior of power systems, neither of these analyses being trivial. This paper presents a survey of the state of the art of these assessments.

Deregulation of electricity industries has created wholesale markets with uncertain prices and offered greater flexibility to investors to make decisions. In this chapter, we consider the problem of a typical investor who has discretion over not only the timing, but also the sizing of a new power plant. The interaction between these two types of managerial flexibility may be addressed analytically using the real options approach. Since an investor may also have discretion over technology choice, we allow for an investment opportunity in two mutually exclusive projects with embedded timing and sizing options. Via numerical examples, we illustrate how an investor may make decisions about timing, sizing, and technology choice. Sensitivity analyses to key parameters also highlight the intuition for how decisions are made.

This chapter develops a decision-making tool to invest in renewable power plants using a real options approach. The model is validated for a wind energy plant. To build a base for the investment model, market prices and wind regimes are obtained from Geometric Brownian Motion (GBM) and Weibull models, respectively. Then, considering these and other values, such as investment, maintenance and operation costs, the Net Present Value (NPV) is obtained. As a result, an NPV curve is drawn by shifting the initial time of investment. From the NPV curve obtained, a trinomial lattice is built and applied to a real options valuation method. From this model, it is possible to estimate the probabilities of investing right now, deferring, or not investing at all. This decision tool allows wind energy investors to decide whether to invest or not in different scenarios. Several realistic case studies are presented to illustrate the decision-making method.

The increasing costs of electricity and the difficulties to expand the power generation capacity, as well as the need for increasing the energy security levels, have enhanced the potential benefits of the electric interconnection among countries and the formation of sub-regional energy markets.

In this context, this paper identifies some sustainable and technically feasible alternatives for electric exchange through interconnections among the electric systems of Bolivia, Chile, Colombia, Ecuador and Peru. In particular, we assess such interconnections from an economic perspective and identify the main barriers for their development. The analysis is carried out at the pre-feasibility level from both private and social point of views, based on the assessment of different investment alternatives in the transmission systems among the aforementioned countries. The modeling of the different future economic operation conditions for each one of the considered electric systems, and for each one of the assessed scenarios, is a central element of the analysis.

We present a multi-year and multi-area dynamic transmission expansion planning model. We define a set of metrics to rate the effect of the expansion among generators and demands. Additionally, we use congestion and saturation indexes, measuring changes in nodal prices and line saturations, respectively. We set the problem in a multi-area framework, assuming different entities in charge of the expansion simultaneously. The proposed formulation results in a mixed-integer linear optimization problem that can be solved using commercially available software. As a result, our model determines the best overall transmission expansion, reflecting both investment and operation costs as well as long-term financial parameters. The described approach is used to identify the most adequate expansion for a generic multi-area system based upon the IEEE 24-bus RTS. We compare the results obtained with individual expansions vs. multi-area expansions in terms of technical and economic indexes.

This chapter presents an overview of algorithms and optimization models used for solving Transmission Expansion Planning (TEP) problem. Being a very complex problem TEP attracts much attention from both researchers and practitioners. A great number of publications in the technical literature address this problem by providing various optimization models and applying different algorithms to solve the TEP problem. Besides literature review and brief classification of the proposed algorithms and models this survey covers examples for most of the methods.

This paper addresses the problem of allocating high-voltage transformer spares (not installed) throughout the electric grid to mitigate the risk of random transformer failures. With this application we investigate the use of approximate dynamic programming (ADP) for solving large scale stochastic facility location problems. The ADP algorithms that we develop consistently obtain near optimal solutions for problems where the optimum is computable and outperform a standard heuristic on more complex problems. Our computational results show that the ADP methodology can be applied to large scale problems that cannot be solved with exact algorithms.

The structure of the electrical system has not changed much since it was first developed: it is characterized by the one-way flow of electricity from centralized power generation plants to users. The smart grid will enable the dynamic, two-way flow of electricity and information needed to support growing use of distributed green generation sources (such as wind and solar), widespread use of electric vehicles, and ubiquitous intelligent appliances and buildings that can dynamically adjust power consumption in response to real-time electricity pricing. The realization of the smart grid is a huge undertaking requiring an unprecedented level of cooperation and coordination across the private and public sectors. A robust, interoperable framework of technical standards is critical to making it happen.

We discuss how to incorporate reliability considerations into power system expansion planning problem. Power system reliability indexes can be broadly categorized as probabilistic and deterministic. Increasingly, the probabilistic criteria have received more attention from the utilities since these can more effectively deal with the uncertainty in system parameters. We propose a stochastic programming framework to effectively incorporate random uncertainties in generation, transmission line capacity and system load for the expansion problem. Favourable system reliability and cost trade off is achieved by the optimal solution. The problem is formulated as a two-stage recourse model where random uncertainties in area generation, transmission lines, and area loads are considered. Power system network is modelled using DC flow analysis. Reliability index used in this problem is the expected cost of load loss as it incorporates duration and magnitude of load loss. Due to exponentially large number of system states (scenarios) in large power systems, we apply sample-average approximation (SAA) concept to make the problem computationally tractable. The method is implemented on the 24-bus IEEE reliability test system.

There is currently a national push to create a smarter, more flexible electrical grid. Traditionally, network branches (transmission lines and transformers) in the electrical grid have been modeled as fixed assets in the short run, except during times of forced outages or maintenance. This traditional view does not permit reconfiguration of the network by system operators to improve system performance and economic efficiency. However, it is well known that the redundancy built into the transmission network in order to handle a multitude of contingencies (meet required reliability standards, i.e., prevent blackouts) over a long planning horizon can, in the short run, increase operating costs. Furthermore, past research has demonstrated that short-term network topology reconfiguration can be used to relieve line overloading and voltage violations, improve system reliability, and reduce system losses. This chapter discusses the ways that the modeling of flexible transmission assets can benefit the multi-trillion dollar electric energy industry. Optimal transmission switching is a straightforward way to leverage grid controllability; it treats the state of the transmission assets, i.e., in service or out of service, as a decision variable in the optimal power flow problem instead of treating the assets as static assets, which is the current practice today. Instead of merely dispatching generators (suppliers) to meet the fixed demand throughout the network, the new problem co-optimizes the network topology along with generation. By harnessing the choice to temporarily take transmission assets out of service, this creates a superset of feasible solutions for this network flow problem; as a result, there is the potential for substantial benefits for society even while maintaining stringent reliability standards. On the contrary, the benefits to individual market participants are uncertain; some will benefit and other will not. Consequently, this research also analyzes the impacts that optimal transmission switching may have on market participants.

Ancillary services are essential for the reliably high-quality operation of a power system. These services are provided by network users and procured by the independent system operator – ISO. Due to system requirements and market structures, ancillary services are managed in different ways around the world. In this chapter, we briefly describe the definition, classification, technical requirements and economic issues of ancillary services. Particularly, we compare active power reserves and reactive support ancillary services in different systems. Finally we show two illustrative examples: A co-optimization model with AC network constraints for the energy and reserve dispatch and a modified version of this model that considers the reactive power dispatch.