شبیه ساز واقع بینانه بازار برق برای مطالعات انرژی و اقتصادی

Electricity market simulators have become a useful tool to train engineers in the power industry. With the maturing of electricity markets throughout the world, there is a need for sophisticated software tools that can replicate the actual behavior of power markets. In most of these markets, power producers/consumers submit production/demand bids and the Market Operator clears the market producing a single price per hour. What makes markets different from each other are the bidding rules and the clearing algorithms to balance the market. This paper presents a realistic simulator of the day-ahead electricity market of mainland Spain. All the rules that govern this market are modeled. This simulator can be used either to train employees by power companies or to teach electricity markets courses in universities. To illustrate the tool, several realistic case studies are presented and discussed.

Power system restructuring has introduced competition in the electricity sector during the last 15 years. Within a market environment, producers compete either in spot markets, such as the pool, or through bilateral contracts. In spot markets, producers’ and consumers’ bids result in clearing prices through an auction mechanism. In many markets, a simple auction algorithm is a key part of the electricity market design. However, a simple auction mechanism does not properly account for complex technical and economical constraints. Building a realistic electricity market simulator is not an easy task [1]. Different markets involve different bidding rules [2] and [3] and different clearing algorithms. In recent years, sophisticated market simulation tools [4] have become available to the power community, some of them making use of JAVA and/or MATLAB-based components [5] and [6], multiagent system technology [7], [8] and [9] and even integrated software platforms [10]. On the other hand, simpler simulators [11] might be appropriate for training purposes, but they might not be accurate enough to be used in industrial environments. Complex bidding rules and realistic market clearing algorithms need to be modeled in detail to analyze and replicate results using real data. This is a novel contribution of the simulator presented in this paper. With the maturing of the electricity markets, companies require tools that can replicate the complex bidding conditions and the clearing of the markets, as is the case of the day-ahead electric energy market in mainland Spain. The simulator described in this paper includes a sophisticated graphical interface that allows its use in industry environments. The contributions of this paper are three-fold: • It provides an electricity market simulator that reproduces realistically the functioning of the electricity market of mainland Spain. • The simulator includes a sophisticated graphic interface that makes it particularly useful in an industrial environment. • The simulator is able to precisely mimic the actual working and the results of the day-ahead energy market within the electricity market of mainland Spain. We believe this paper might encourage the development of other simulators mimicking electricity markets elsewhere. This might result in better training of operators and better understanding of electricity markets. The electricity market in Spain was launched in January 1998, after a new electricity law was released at the end of 1997. It constituted a new legal and institutional framework that introduced competition amongst producers and consumers. The former cost minimization model was replaced by a model where economic agents optimized their decisions independently. In this new market structure, economic agents are generators, distributors,1 retailers, qualified customers and external agents. Since 1998, four markets compose the electricity market framework in Spain: (i) the day-ahead market, (ii) the ancillary services market, (iii) the hour-ahead market and (iv) the real-time imbalance correction process [12] and [13]. The most important one is the day-ahead market, managed by the Market Operator (MO). Once the day-ahead market is cleared a technical constraints solution process is carried out by the Independent System Operator (ISO), who is in charge of the technical aspects of the market. The day-ahead market is cleared on an hourly basis. The generators sell their production using energy bids, and qualified consumers, distribution companies and retailers buy energy using purchase bids. Although the format of the bids may look simple in principle, this realistic market allows including more complex restrictions in the bidding process, both technical and economical. The most important extra bidding conditions that are allowed by the day-ahead Spanish market are: non-divisible quantity, ramp-up and ramp-down rate limits and minimum revenue requirement. A detailed account of these conditions is presented in the next section. In the day-ahead market, a supply curve is built up for each hour considering the selling bids ordered by increasing prices and also a demand curve is built up considering the buying bids ordered by decreasing prices. The intersection of the supply and demand curves determines the selling and buying bids that are accepted; the hourly market price is the price of the last accepted selling bid. This process results in a uniform price for every hour. Additional conditions imposed by the extra bidding constraints are solved using a repair heuristic algorithm [14]. This algorithm resolves the technical infeasibilities that may occur because of violation of complex bidding conditions. Within the framework of the Spanish electricity market, this paper provides a realistic simulator that reproduces the main features of the sophisticated day-ahead electric energy market. The paper is organized as follows. Section 2 presents the rules of the Spanish electricity market on which the market simulator is based, as well as several illustrative examples where the effect of the extra bidding conditions on prices and energies is shown, Section 3 shows several case studies based on realistic scenarios and Section 4 provides some relevant conclusions.

This paper presents a realistic simulator for the day-ahead electricity market of mainland Spain. This simulator contains most of the actual features that govern this complex day-ahead market. It can be used for training purposes both in industry and academia. Interested agents can analyze their corresponding economic and energy results to improve subsequent bidding rounds in the actual day-ahead market. The simulator can be a useful tool for the generating companies, since they can study different scenarios, modifying the way in which they offer their energy on the market in order to maximize their profits. The simulator can also be a useful tool for the buyers, allowing them to test several feasible bidding strategies, according to their own preferences. Another interesting application of the simulator consists in determining the effect of technical and economic constraints (associated to selling bids) in hourly prices. However, this effect is not very relevant in the Spanish day-ahead market. The case studies are significantly complex and the execution times are reasonably small for the simulator to be used on a daily basis by electricity companies.