Salvador Pineda

Electricity market clearing with improved scheduling of stochastic production

In this paper, we consider an electricity market that consists of a day-ahead and a balancing settlement, and includes a number of stochastic producers. We first introduce two reference procedures for scheduling and pricing energy in the day-ahead market: on the one hand, a conventional network-constrained auction purely based on the least-cost merit order, where stochastic generation enters with its expected production and a low marginal cost; on the other, a counterfactual auction that also accounts for the projected balancing costs using stochastic programming. Although the stochastic clearing procedure attains higher market efficiency in expectation than the conventional day-ahead auction, it suffers from fundamental drawbacks with a view to its practical implementation. In particular, it requires flexible producers (those that make up for the lack or surplus of stochastic generation) to accept losses in some scenarios. Using a bilevel programming framework, we then show that the conventional auction, if combined with a suitable day-ahead dispatch of stochastic producers (generally different from their expected production), can substantially increase market efficiency and emulate the advantageous features of the stochastic optimization ideal, while avoiding its major pitfalls.

Real-Time Market Concept Architecture for EcoGrid EU—A Prototype for European Smart Grids

Industrialized countries are increasingly committed to move towards a low carbon generating mix by increasing the penetration of renewable generation. Additionally, the development in communication technologies will allow small end-consumers and small-scale distributed energy resources (DER) to participate in electricity markets. Current electricity markets need to be tailored to incorporate these changes regarding how electricity will be generated and consumed in the future. The EcoGrid EU is a large-scale EU-funded project, which establishes the first prototype of the future European intelligent grids. In this project, small-scale DERs and small end-consumers can actively participate in a new real-time electricity market by responding to 5-min real time electricity prices. In this way, the market operator will also obtain additional balancing power to cancel out the production variation introduced by renewable electricity generation. The real-time market concept architecture for EcoGrid EU is introduced in this paper, which provides a market-based platform and information and communication technology (ICT) infrastructure that extends the current electricity market to a shorter time horizon and to smaller assets.

Ecogrid EU - a large scale smart grids demonstration of real time market-based integration of numerous small DER and DR

This paper provides an overview of the Ecogrid EU project, which is a large-scale demonstration project on the Danish island Bornholm. It provides Europe a fast track evolution towards smart grid dissemination and deployment in the distribution network. Objective of Ecogrid EU is to illustrate that modern information and communication technology (ICT) and innovative market solutions can enable the operation of a distribution power system with more than 50% renewable energy sources (RES). This will be a major contribution to the European 20-20-20 goals. Furthermore, the proposed Ecogrid EU market will offer the transmission system operator (TSO) additional balancing resources and ancillary services by facilitating the participation of small-scale distributed energy resources (DERs) and small end-consumers into the existing electricity markets. The majority of the 2000 participating residential customers will be equipped with demand response devices with smart controllers and smart meters, allowing them to respond to real-time prices based on their pre-programmed demand-response preferences.

Impact of forecast errors on expansion planning of power systems with a renewables target

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a programming modeling framework to determine the generation and transmission expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study.

Redefining the Merit Order of Stochastic Generation in Forward Markets

This letter proposes a new merit order for the dispatch of stochastic production in forward markets (e.g., day-ahead markets). The proposed merit order considers not only the marginal cost of the stochastic generating unit, which is often very low or zero, but also the projected cost of balancing its energy deviations during the real-time operation of the power system. We show, through an illustrative example, that the proposed merit order leads to increased market efficiency as the penetration of stochastic generation in the electricity market grows.

Capacity expansion of stochastic power generation under two-stage electricity markets

Energy imbalances due to power forecast errors have a significant impact on both the cost of operating the power system and the profitability of stochastic power generating units. In this paper, we propose a modeling framework to analyze the effect of the costs of these imbalances on the expansion of stochastic power generating units. This framework includes the explicit representation of a day-ahead and a balancing market-clearing mechanisms to properly capture the impact of forecast errors of power production on the short-term operation of a power system. The proposed generation expansion problems are first formulated from the standpoint of a social planner to characterize a perfectly competitive market. We investigate the effect of two paradigmatic market designs on generation expansion planning: a day-ahead market that is cleared following a conventional cost merit-order principle, and an ideal market-clearing procedure that determines day-ahead dispatch decisions accounting for their impact on balancing operation costs. Furthermore, we reformulate the proposed models to determine the optimal expansion decisions that maximize the profit of a collusion of stochastic power producers in order to explore the effects of competition at the investment level. The proposed models are first formulated as multi-level programming problems and then recast, under certain assumptions, as single-level mixed-integer linear or non-linear optimization problems using duality theory. The variability of the forecast of the stochastic power production and the demand level throughout the planning horizon is modeled using yearly duration curves. Likewise, the uncertainty pertaining to power forecast errors is characterized through scenario sets. The main features and results of the proposed models are discussed using an illustrative example and a more realistic case study based on the Danish power system. HighlightsA two-stage market properly models the effects of forecast errors on system operation.Forecast errors highly affect investment decisions of stochastic power generation.A market that efficiently handles forecast errors increases the installed capacity.The exercise of market power at the investment level reduces the installed capacity.The proposed modeling of a two-stage market reduces the computational burden

On the inefficiency of the merit order in forward electricity markets with uncertain supply

This paper provides insight on the economic inefficiency of the classical merit-order dispatch in electricity markets with uncertain supply. For this, we consider a power system whose operation is driven by a two-stage electricity market, with a forward and a real-time market. We analyze two different clearing mechanisms: a conventional one, whereby the forward and the balancing markets are independently cleared following a merit order, and a stochastic one, whereby both market stages are co-optimized with a view to minimizing the expected aggregate system operating cost. We first derive analytical formulae to determine the dispatch rule prompted by the co-optimized two-stage market for a stylized power system with flexible, inflexible and stochastic power generation and infinite transmission capacity. This exercise sheds light on the conditions for the stochastic market-clearing mechanism to break the merit order. We then introduce and characterize two enhanced variants of the conventional two-stage market that result in either price-consistent or cost-efficient merit-order dispatch solutions, respectively. The first of these variants corresponds to a conventional two-stage market that allows for virtual bidding, while the second requires that the stochastic power production be centrally dispatched. Finally, we discuss the practical implications of our analytical results and illustrate our conclusions through examples.

Renewable generation expansion under different support schemes: A stochastic equilibrium approach

Abstract Following the deregulation of electricity markets, a current challenge of policy makers is to facilitate the transition to a sustainable power system at the highest welfare for society. In this paper we investigate the efficiency of different support schemes, such as a feed-in tariff, a feed-in premium and tradable green certificates, with respect to incentivizing the required investments in renewable generation. We consider a number of generation expansion problems, and formulate stochastic equilibrium models that account for uncertainty in demand and renewable supply, the risk-aversion of investors and the competitiveness of the market. The problem of the policy maker is formulated as a mathematical program with equilibrium constraints (MPEC) and as a non-linear complementarity problem (NCP) for the feed-in schemes and the certificate market, respectively. Our models are solved for a small illustrative example and a larger case study based on the Danish power system. The results confirm that the main driver for the optimal choice of renewable support scheme is the aversion of power producers towards price and volume risk, while the competitiveness of the market rarely affects such choice.

Enabling demand response by extending the European electricity markets with a real-time market

The EcoGrid concept proposes to extend the current wholesale electricity market to allow participation of Distributed Energy Resources (DERs) and domestic end-consumers in system balancing. Taking advantage of the smart grid technology, the EcoGrid market publishes the real-time prices that entail an appropriate response of DERs and flexible customers to cope with the production deviation of renewable generating units. In the EcoGrid model the relation between the retailer and the customer stays entirely in the liberalized market, opening opportunities for new retail-level products and contracts supporting desired trade-offs of risk and benefit levels. The concept increases the market value of wind power, which in the long run is expected to provide the economic incentives to a higher wind power penetration. Hence, for a Europe-wide uptake of demand response, a good accordance between the EcoGrid concept and the new ENTSO-E Network Code on Electricity Balancing will be vitally important.

The impact of dynamic electricity tariff on long-run incremental cost

Electricity plays an important role in the future energy framework around the world. The foreseen high penetration of renewable energy resources and electric vehicles (EV) will change the way of understanding and operating power systems. Consequently, significant investment in network infrastructure needs to be made in order to cope with this tremendous change in an efficient and effective manner. Longrun incremental cost (LRIC) pricing method is recognized as an economically efficient approach for pricing network charges, which provides forward-looking information for future investment cost. LRIC evaluation is usually conducted on the basis that demand is passive and uncontrollable. The impact of demand flexibility on LRIC has not been comprehensively studied. In this paper, the effect of dynamic electricity tariff and flexible demand on LRIC and network investment decisions is deeply analyzed and discussed. A modified test system (RBTS) illustrates the proposed method.