Enzo Sauma

Economic Criteria for Planning Transmission Investment in Restructured Electricity Markets

From an economic perspective, a common criterion for assessing the merits of a transmission investment is its impacts on social welfare. The underlying assumption in using this criterion is that side payments may be used to distribute the social gains among all market players. In reality, however, since the impacts of an electricity transmission project on different players may vary, such side payments are rather difficult to implement. This paper focuses on different economic criteria that should be considered when planning electricity transmission investments. We propose an electricity transmission investment assessment methodology that is capable of evaluating the economic impacts on the various effected stakeholders and account for strategic responses that could enhance or impede the investments objectives. We formulate transmission planning as an optimization problem under alternative conflicting objectives and investigate the policy implications of divergent expansion plans resulting from the planners level of anticipation of strategic responses. We find that optimal transmission expansion plans may be very sensitive to supply and demand parameters. We also show that the transmission investments have significant distributional impact, creating acute conflicts of interests among market participants. We use a 32-bus representation of the main Chilean grid to illustrate our results.

A Three-Level Static MILP Model for Generation and Transmission Expansion Planning

We present a three-level equilibrium model for the expansion of an electric network. The lower-level model represents the equilibrium of a pool-based market; the intermediate level represents the Nash equilibrium in generation capacity expansion, taking into account the outcomes on the spot market; and the upper-level model represents the anticipation of transmission expansion planning to the investment in generation capacity and the pool-based market equilibrium. The demand has been considered as exogenous and locational marginal prices are obtained as endogenous variables of the model. The three-level model is formulated as a mixed integer linear programming (MILP) problem. The model is applied to a realistic power system in Chile to illustrate the methodology and proper conclusions are reached.

Unit Commitment With Ideal and Generic Energy Storage Units

Intermittence and variability of renewable resources is often a barrier to their large scale integration into power systems. We propose a stochastic real-time unit commitment to deal with the stochasticity and intermittence of non-dispatchable renewable resources including ideal and generic energy storage devices. Firstly, we present a mathematical definition of an ideal and generic storage device. This storage device definition has some mathematical advantages: 1) it can be easily integrated within complex optimization problems, 2) it can be modeled using linear programming, suitable for practical large-scale cases. Secondly, a stochastic unit commitment with ideal and generic storage devices and intermittent generation is proposed to solve the joint energy-and-reserves scheduling and real-time power balance problem, reflecting the minute-by-minute intermittent changes and the stochasticity of renewable resources. We also compare our results with those obtained using a deterministic unit commitment with perfect information. The proposed model is illustrated with a 24-bus system.

Unit commitment with ideal and generic energy storage units

Summary form only given. Intermittence and variability of renewable resources is often a barrier to their large scale integration into power systems. We propose a stochastic real-time unit commitment to deal with the stochasticity and intermittence of non-dispatchable renewable resources including ideal and generic energy storage devices. Firstly, we present a mathematical definition of an ideal and generic storage device. This storage device definition has some mathematical advantages: 1) it can be easily integrated within complex optimization problems, 2) it can be modeled using linear programming, suitable for practical large-scale cases. Secondly, a stochastic unit commitment with ideal and generic storage devices and intermittent generation is proposed to solve the joint energy-and-reserves scheduling and real-time power balance problem, reflecting the minute-by-minute intermittent changes and the stochasticity of renewable resources. We also compare our results with those obtained using a deterministic unit commitment with perfect information. The proposed model is illustrated with a 24-bus system.

Ex-Ante Assessment of the Implementation of an Energy Efficiency Certificate Scheme in Chile

AbstractEnergy efficiency (EE) policies are primarily implemented in developed countries. However, the benefits of implementing them in developing countries may be quite large. In this article, the potential implementation of an EE certificates scheme in Chile is studied. In particular, the white-certificate mechanism (which is a credit-based trading mechanism) used in Italy to promote EE is studied and its implementation in Chile through an ex-ante simulation is considered. The purpose of this mechanism is for the regulation authority to set a fixed amount of energy savings (which minimizes the total cost of producing the needed energy supply) and obligate gas and electricity distribution companies to obtain enough certificates to reach that target. Several benefits of reducing energy consumption is studied using a social-economic project assessment methodology. As a result, large economic benefits of implementing a white-certificate scheme in Chile were found.

Valuation of the Economic Impact of the Initial Allocation of Tradable Emission Permits in Air Pollution Control

One of the key concerns about applying emission permits systems in air pollution control is the initial allocation of entitlements. In this paper, a model that captures the interaction between strategic firms and a permit-allocating authority to assess social-welfare implications of the initial allocation regimes of tradable emission permits is introduced. In particular, a three-period model is formulated for studying how the exercise of market power by oligopolistic firms affects the pollution control technology investments and, in this way, the valuation of different initial allocation proposals. The analysis shows that a proactive allocation of initial entitlements may improve social welfare with respect to a distribution that ignores the interactions between the initial allocation of emission permits and the firms’ strategic response in pollution control technology investment. The results are illustrated in the context of a sulfur dioxide emission permits system for the thermal electricity generation ...

Basic theoretical foundations and insights on bilevel models and their applications to power systems

Decision making in the operation and planning of power systems is, in general, economically driven, especially in deregulated markets. To better understand the participants’ behavior in power markets, it is necessary to include concepts of microeconomics and operations research in the analysis of power systems. Particularly, game theory equilibrium models have played an important role in shaping participants’ behavior and their interactions. In recent years, bilevel games and their applications to power systems have received growing attention. Bilevel optimization models, Mathematical Program with Equilibrium Constraints and Equilibrium Problem with Equilibrium Constraints are examples of bilevel games. This paper provides an overview of the full range of formulations of non-cooperative bilevel games. Our aim is to present, in an unified manner, the theoretical foundations, classification and main techniques for solving bilevel games and their applications to power systems.

A Robust optimization approach to assess the effect of delays in the connection-to-the-grid time of new generation power plants over transmission expansion planning

Construction times of new power plants are subject to uncertainty in deregulated electricity markets and they have an important impact on the planning of the expansion of the transmission network. In this paper, we develop a model of transmission expansion planning that is based on Robust optimization and considers the uncertainty in the instant when new power plants are connected into the electricity grid. Using this transmission expansion planning model, we assess the impact of postponing the connection time of some new power plants over the system cost and the optimal network expansion plan. By obtaining the optimal solutions of the proposed model for different levels of robustness, we are able to rank the new power plants with respect to how much the delay of each project negatively affects the system cost. We illustrate our methodology and the performance of two solution approaches proposed to solve our model by using a 34-node stylized version of the main Chilean grid. In the case of the main Chilean grid, the results obtained indicate that some of the projects whose delays negatively affect the most to the system cost are the renewable power plants projected for the next ten years. This is important from the regulator viewpoint because it allows the assessment of renewable energy policies that accelerate the installation of renewable new power plants also in terms of their impact over the transmission expansion planning.

An Exergy and Homeostatic Control Approach to Sustainable Grid-Connected Microgrids without Energy Storage

This paper approaches the microgrid concept from a systemic and cybernetics viewpoint, as a viable sustainable energy system (SES) for supplying electricity and heat to small, rural communities in Chile. As such the microgrid may be viewed as a complex adaptive system (CAS) when connected to the grid and operating without energy storage, only with the grid as back-up power source. From an exergy and homeostatic control (HC) standpoint, one may analyze the microgrid as a socio-technical CAS when it is coupled with a set of homes and also connected to the grid; capable of supplying close to 80% of the daily residential consumer needs on average. Thus, under these conditions homes may draw inexpensive renewable electricity and heat from the microgrid if they are thrifty and efficient in their energy consumption, and they may also consume from the utility grid, yet at an expensive price. Based on this an exergy and HC approach is proposed to develop such SES for rural and remote communities in Chile and South America, aiming to enhance energy efficiency (EE) and energy sustainability (ES). Under this scheme, renewable power (RP) being a scarce resource that must be managed efficientlyis supplied only to homes which comply with a specific criterion in an effort to curtail demand to ensure ES overtime. Simulation shows as expected that indeed certain criteria produce much better results than others in incentivizing thrifty, efficient energy consumption for small-size communities without the need for expensive and sometimes polluting and forbidden energy storage systems.

A Survey and Comparison of Optimization Methods for Solving Multi-Stage Stochastic Programs with Recourse

In the last decade, multi-stage stochastic programs with recourse have been broadly used to model real-world applications. This paper reviews the main optimization methods that are used to solve multi-stage stochastic programs with recourse. In particular, this paper reviews four types of optimization approaches to solve multi-stage stochastic programs with recourse: direct methods, decomposition methods, Lagrangian methods and empirical-distribution methods. All these methods require some form of approximation, since multi-stage stochastic programs involve the evaluation of random functions and their expectations. The authors also provides a classification of the considered optimization methods. While decomposition optimization methods are recommendable for large linear problems, Lagrangian optimization methods are appropriate for highly nonlinear problems. When the problem is both highly nonlinear and very large, an empirical-distribution method may be the best alternative.