Electricity pricing strategy in Iran's competitive electricity market using genetic algorithm

Master's Thesis of Energy Systems Engineering

Abstract

In the process of restructuring the electricity market, the pricing methods of power producers are the most important factor to obtain more profit, and for this reason, many researches have been conducted with different approaches such as the optimization approach in the field of determining pricing strategies. In this research, it has been tried to look at this problem by using the applied concepts of genetic algorithm and extracting a suitable model from the neural network. In fact, the innovation of this research, in addition to presenting a new plan to solve the problem of model optimization, comparing and testing the actual software outputs in the Iranian electricity market, and looking specifically at the applicability of solving the problem using the proposed algorithm and economic concepts. In this research, after the introduction of Iran's electricity market and its governing structure, a new plan has been presented to optimize the electricity sales strategy by power plant owners and producers, so that the new plan and algorithm are first introduced, and then the target function we want in future optimization is modeled in the neural network, so that by having a suitable and capable model in the description of the system, an acceptable optimization can be done using the genetic algorithm. In the end, in a voluntary initiative and using the confidence and cooperation of one of the country's combined cycle power plants, the outputs of the proposed algorithm were recorded in the pricing system of the country's power plants, and the results obtained based on this plan were tested in the real environment of the Iranian electricity market, and fortunately, the results exceeded the expectations of the research. The proof of this claim is the output reports of the pricing system of the Ministry of Energy, which is included in the chapter related to the results of this thesis. This issue confirms the strengths of this modeling and optimization.

Key words: optimal price, pricing strategies, neural network, genetic algorithm, Iranian electricity market

1-1

Electricity markets are being launched in order to create competition all over the world. to be The main goal of competition in these markets is to make the competition environment and market concept more effective in them. The general concept of investment, if the market structures are fair and just, is to give an incentive to the companies active in the market to maximize their profits, and then the market behaves in a way that the profit of each company is maximized (or it is subject to profit and loss according to its activities and decisions). If this goal is to be achieved, the power industry will need new algorithms to help market companies maximize their profits. In addition to modeling the economic aspects of electricity markets, these algorithms must also provide basic engineering requirements.

In order to enter into specialized discussions about the Iranian electricity market, modeling a specific objective function, proposing a new and reasonable algorithm, and solving and optimizing the proposed model, one must know the Iranian electricity market and be fully familiar with its structure.

Owners of power plants in Iran can to sell in various ways, among the most important types of sales contracts, the following can be mentioned, which are generally used by power plants to enter into a sales contract with the Ministry of Energy (Tavanir Company and Iran Electricity Network Management Company): Bilateral

Cross-border sales contracts

Energy conversion contracts[1]

The wholesale contract in the competitive market is actually the same dynamic market in which the competition over the final price of electricity sales is conducted between buyers (regional electricity) and electricity sellers (power plants), and the subject of this thesis It has been modeled and optimized in this space.. A guaranteed purchase contract is a contract that is concluded between a seller and a buyer and is traded at a fixed rate of electrical goods. In this type of contract, there is practically no risk, and in order not to bear the risk, the selling price of electricity is lower than the tempting rates in the competitive electricity market, so that usually privately owned power plants are less satisfied with selling their products at guaranteed rates approved by the Ministry of Energy.

Bilateral contracts are also concluded between a producer and a consumer so that they only use the national power grid for energy transfer and pay a fee as a transit fee in return for this use. Cross-border electricity sales contracts are concluded with electricity applicants outside the borders of our beloved country after obtaining the necessary permits from the Ministry of Energy and are issued in some way. Also, energy conversion contracts have been concluded between some power plants and the Ministry of Energy, and regardless of the price of electricity produced and the cost of fuel consumed and a series of agreed costs, the power plant will have only one income for converting consumed fuel into produced electricity. Considering the establishment of the wholesale electricity market in our country since November 2012 under the supervision of the Iranian Electricity Market Regulation Board and the laws approved by this board, the issue of offering the optimal price for the sale of electricity by producers (power plants) as well as buyers (regional electricity companies) has become especially important in recent years. The minimum price (235 rials per kilowatt at the moment) for power plant sales has been considered by the Iranian Electricity Market Regulation Board and the power plants predict the price of the next three days in the software provided by the market every day and express their readiness to participate in the grid.

The country and according to the suggestions of buyers and sellers (market players), the production arrangement for power plants will be announced in the coming days. The logic of the market is to minimize the costs of purchasing electricity, transmission and losses of the national electricity network, so that the proposed prices of power plants are arranged from low to high, and the proposal of power plants will naturally be accepted to the extent that the country's electricity consumption is provided based on the declaration of regional electricity needs, so it makes sense to win and lose.

Due to the trend towards privatization in the electricity industry, the desire to earn more profit in exchange for higher risk has increased and the actors of the electricity market They seek to discover the settlement point of the market. In Section 2-1, this very important point is introduced further.

Figure 1-1: Supply and demand curve and market settlement point

Many factors are involved in complicating the problem and making it difficult to obtain an optimal price. The geographical location where power plants and regional power companies are located, the prices offered by other players in the electricity market, the price of fuel consumption determined based on the approvals, the hours of the day, the days of the week, the official holidays of the country (for example, the minimum annual consumption of our country occurs on the days of Ashura and Tasua), the pattern of people's consumption that changes over time, the limitations that exist technically for power plants (such as: the trans capacity of power plants, the limit of water behind a dam of a hydro power plant, the common electricity input substation to the network for several power plants) and . Among these things.

One ??of the difficulties in solving this problem is how to model and extract mathematical equations and relationships from these abstract concepts, which can be justified due to the existence of courses such as advanced mathematical programming, energy systems analysis, the basics of economics that exist in the field of energy systems engineering, and the basis of conducting such research in this field of study.