Javier Reneses

A new approach to model load levels in electric power systems with high renewable penetration

In medium- and long-term power system models, it is a common approach to approximate the demand curve by load levels in order to make the models computationally tractable. However, in such an approach, the chronological information between individual hours is lost. In this paper, we propose a novel approach to power system models which constitutes an alternative to the traditional load levels. In particular, we introduce the concept of system states as opposed to load levels, which allows us to better incorporate chronological information in power system models, thereby resulting in a more accurate representation of system outcomes such as electricity prices and total cost. Moreover, the system states can be defined taking into account various important system features at once, as opposed to load levels which are defined using just one specific feature, i.e., demand or net demand. Therefore the system states approach better captures other results such as reserve prices, which are not driven by the usual feature used to define load levels. In a case study, we compare the newly proposed methodology to a standard load level approach, which validates that the system states approach better captures power system outcomes.

Coordination between medium-term generation planning and short-term operation in electricity markets

This paper analyzes the coordination between medium-term generation planning and short-term operation in electricity markets. This coordination is particularly important from a practical point of view in order to guarantee that certain aspects of the operation that arise in the medium-term level are explicitly taken into account: limited-energy resources and obligatory-use resources. Three different approaches are proposed in order to guarantee that short-term decisions made by a generation company are consistent with its operation objectives formulated from a medium-term perspective. These approaches make use of technical and economic signals to coordinate both time scopes: primal information, dual information, and resource-valuation functions. This paper presents the main advantages and drawbacks of the three approaches and applies them to a case study that uses a conjectural-variation-based representation of the market.

Stochastic Market Equilibrium Model For Generation Planning

It is widely accepted that medium-term generation planning can be advantageously modeled through market equilibrium representation. There exist several methods to define and solve this kind of equilibrium in a deterministic way. Medium-term planning is strongly affected by uncertainty in market and system conditions, thus extensions of these models are commonly required. The main variables that should be considered as subject to uncertainty include hydro conditions, demand, generating units failures and fuel prices. This paper presents a model to represent medium-term operation of electrical market that introduces this uncertainty in the formulation in a natural and practical way. Utilities are explicitly considered to be intending to maximize their expected profits and biddings are represented by means of a conjectural variation. Market equilibrium conditions are introduced by means of the optimality conditions of a problem, which has a structure that strongly resembles classical optimization of hydrothermal coordination. A tree-based representation to include stochastic variables and a model based on it are introduced. This approach for market representation provides three main advantages: robust decisions are obtained; technical constraints are included in the problem in a natural way, additionally obtaining dual information; and big size problems representing real systems in detail can be addressed.

Long-term market equilibrium modeling for generation expansion planning

This paper introduces a methodology to assist generation companies to deal with planning decisions in a competitive framework. It is based on a two-stage representation of market equilibrium. A first level computes an approximate continuous Cournot equilibrium for the entire model horizon. The second one discretises this solution separately for each year. Some aspects of its practical implantation are discussed.

Electricity and Natural Gas Interdependency: Comparison of Two Methodologies for Coupling Large Market Models Within the European Regulatory Framework

Power generation growth based on natural gas fired power plants (NGFPPs) has lead to increasing interactions between electric power and natural gas industries. More companies are progressively and simultaneously participating as big players in both markets. However, each company has traditionally been settled in one side, holding a particular competitive advantage: electric power generation companies mainly know how to operate their generation assets, whereas gas companies mainly know how to manage their gas supply contracts and make use of often regulated gas assets. Multi-product energy companies have even created independent departments which decisions are usually taken uncoordinatedly. In any case, companies (or departments) usually support their decision-making process in mathematical tools which represent each market with detail. This paper presents two methodologies for coupling two interdependent electricity and gas market models formulated as optimization problems. Each methodology fulfills different department wishes. The “electricity-perspective” methodology maximizes electricity market profits after calculating equivalent gas contracts with the gas market model. In contrast, the “gas-perspective” methodology minimizes gas operation costs after obtaining the relationship between the marginal revenue and the gas consumption with the electricity market model. This coordinated solution would allow companies to obtain synergies, resulting in a competitive advantage over other companies that operate uncoordinatedly in both markets.

Gas–Electricity Coordination in Competitive Markets Under Renewable Energy Uncertainty

As climate concerns, low natural gas prices, and renewable technologies increase the electric power sectors dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas-electricity models exist. This paper reviews the state of the art for hybrid gas-electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources.

Strategic Management of Multi-Year Natural Gas Contracts in Electricity Markets

Combined cycle gas turbine (CCGT) plants show some advantages, such as better economies of scale, or lower CO2 emission rates, in comparison to other technologies. In addition, due to their flexible operation, CCGT plants are a useful support for the integration of the growing renewable energy installed capacity. Consequently, during the last years, CCGT plants have proliferated in electricity systems, increasing the global demand of natural gas (NG). In order to guarantee the NG supply and to hedge the price volatility, electricity generation companies (Gencos) sign supply contracts with NG producers. Typically, NG producers force long-term contracts in order to recover their huge capital investments. Therefore, Gencos should optimize the exercise of the supply contracts in the long-term scope in order to maximize their profits in the electricity market. However, the optimal exercise of the supply contracts on behalf of the Gencos could be impeded because of possible bottlenecks in the NG system. Accordingly, this paper proposes a methodology to incorporate both the characteristics of NG supply contracts and the congestions in the NG system in an electricity market model that could support the decision-making process on behalf of the Gencos. A study case illustrates the methodology.

Optimizing Storage Operations in Medium- and Long-Term Power System Models

In this paper, we propose a new methodology to formulate storage behavior in medium- and long-term power system models that use a load duration curve. Traditionally in such models, the chronological information among individual hours is lost; information that is necessary to adequately model the operation of a storage facility. Therefore, these models are not fully capable of optimizing the actual operation of storage units, and often use pre-determined data or some sort of peak-shaving algorithm. In a rapidly changing power system, the proper characterization of storage behavior and its optimization becomes an increasingly important issue. This paper proposes a methodology to tackle the shortcomings of existing models. In particular, we employ the so-called system states framework to recover some of the chronological information within the load duration curve. This allows us to introduce a novel formulation for storage in a system states model. In a case study, we show that our method can lead to computational time reductions of over 90% while accurately replicating hourly behavior of storage levels.

Distribution Pricing: Are We Ready for the Smart Grid?

Energy transportation costs typically make up a quarter of consumers? electricity bills, and most of this amount (90% in the United Kingdom, 75% in Brazil and Spain, and 60% in India, for example) is due to energy transportation through the distribution network. This cost could escalate over the next few decades as distributed energy resources are expected to grow substantially in response to the financial incentives many governments have created for renewable and efficient generation to meet their CO2 reduction targets.

Medium-Term Probabilistic Forecasting of Electricity Prices: A Hybrid Approach

This paper provides a focus upon forecasting electricity prices in the medium term (from a few weeks to several months ahead) in which accurate estimates of tail risks, e.g., at the 1%, 5%, 95%, and 99%, are important. Medium term forecasting and risk analysis are important for operational scheduling, fuel purchasing, trading, and profit management. We extend the research on hybrid forecasting methods, which link detailed fundamental price formation models, using optimization techniques and market equilibrium considerations, with econometric recalibration to the time series data. This paper is innovative in its use of quantile regression to undertake the recalibration and provide accurate risk estimates. It is shown that probabilistic outputs from the fundamental model add value over expected value inputs to the quantile regressions and that if the fundamental model is itself well specified to diurnal variation through the inclusion of relevant explanatory variables such as demand or climatic conditions, then it is not necessary to undertake the quantile regressions separately for each hour of the day. A real application of the proposed methodology is successfully tested on the Spanish electric power system, in which the high penetration of intermittent wind generation creates extreme price risks. The hybrid method outperforms the more conventional fundamental model, making particular use of wind generation data in the quantile recalibrations.