Independent Market Operator Research Articles

The effect of responsive demand penetration on key parameters of the two-sided market by using structural decomposition of the electricity market

Today, with the development of price-responsive demand programs in the electricity industry and consumers’ desire for it, two-sided market is more prepared than before. In a two-sided market, factors such as responsive demand or congestion in transmission lines change market variables (unit generation, price per bus). Recognizing these changes helps the independent market operator to have proper market control. The primary purpose of this study is to investigate and quantify the effect of presence and increase of the penetration of responsive demand in the network on critical components of the market, including locational marginal prices (LMPs), power generation of units, power consumption of responsive demand, power flow of lines and congestion. To achieve this goal, after identifying and classifying the problem parameters in the form of quadratic programming (QP) problem, structural decomposition is implemented by using the Lagrange function and the Kuhn–Tucker (KKT) conditions to decompose all variables of the electricity market, such as responsive demand sensitivity, electricity price, and positive duality of generation units, into key parameters to calculate the share of individual key market parameters in the two-side electricity market. Finally, the sensitivity of market variables to price-responsive demand parameters has been investigated. The proposed approach is evaluated on the IEEE 24-bus system, and the results are discussed.

Research on the Electricity Market Clearing Model for Renewable Energy

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers.

Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier’s bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms.

Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty

This paper addresses the energy dispatch problem for multi-stakeholder multiple microgrids (MMGs) under uncertainty while considering independent market operators (IMOs) based energy trading forms. Firstly, a collaborative hierarchical dispatch framework is proposed to adapt to decentralized multiple stakeholders and coordinate energy trading between IMOs and microgrids (MGs). And then this framework is further decomposed into different independent optimization problems for stakeholders based on an analytical target cascading (ATC) algorithm, in which Lagrangian penalty terms are introduced to ensure consistency in energy trading. In these optimization problems, energy trading and production of an individual MG is formulated as a two-stage adaptive robust optimization model to hedge against uncertainties from random renewable energy sources and loads. Moreover, in order to realize parallel computing for all independent optimization problems, a diagonal quadratic approximation method is applied to linearize quadratic terms. We integrate the ATC algorithm with a column-and-constraint generation algorithm to derive robust energy dispatch schemes in parallel. Finally, simulations on different cases are conducted to testify the rationality and validity of the proposed robust distributed energy dispatch approach. The results show that the hierarchical energy dispatch framework with IMOs has advantages over that without IMOs. Moreover, the proposed approach can reduce the impacts of uncertainties on distributed decision making of multiple stakeholder and enhance the computational efficiency.

A new mechanism for remedial action schemes design in a multi-area power system considering competitive participation of multiple electricity market players

Unlike to common protective devices that are used for protection of power system equipment, Special Protection System (SPS) means an automated protective system which detects abnormal and predetermined conditions of the power system and applies preplanned corrective actions in order to maintain system integrity. The SPS is widely used in a restructured environment because it is known as a rapid and low cost alternative for network expansion to increase the transfer capability. Although the economic motivations and technical aspects of SPS are proposed in some references, the economic design of an SPS in a restructured environment has received less attention. In this paper, an economic-based method is presented to design remedial action schemes in a Multi-Area Power System (MAPS) with presence of Multiple Electricity Markets (MEM). The innovations of the proposed method are twofold: on one hand, the protection schemes are updated after each electricity market clearing that increases its performance because of the proximity to real time conditions. On the other hand, these remedial actions are designed economically in an environment with several independent Market Operators (MOs) through the interaction with a central coordinator entity. The proposed method is applied on a three-area test system and its results are presented by further explanation of its technical and economic advantages.

Analysing the European Commission’s Final Decisions on Apple, Starbucks, Amazon and Fiat Finance & Trade

This article addresses the issues which stem from the State aid investigations opened by the European Commission (EC) against Luxembourg, Ireland and the Netherlands, concerning aid granted in the form of tax base reduction to certain undertakings, namely Apple Inc., Amazon, Starbucks and Fiat Finance and Trade (FFT). Furthermore, this article analysis the EC’s main line of argument in the aforementioned State aid investigations that is, acceptance by the Netherlands, Ireland and Luxembourg, of the proposed calculation of the taxable base of these multinational enterprises (MNEs) which do not reflect normal market conditions, may result in State aid, in that it will offer a more favourable treatment to the MNE compared to the treatment other undertakings would normally receive under the Member States’ ‘normal’ tax system. In arriving at what would constitute normal market conditions, the EC uses two benchmarks namely, the internationally accepted standard, the Arm’s Length Principle (ALP) and the Prudent Independent Market Operator (PIMO), which makes its first appearance in these investigations.

Cobweb theory‐based generation maintenance coordination in restructured power systems

A cobweb theory-based maintenance coordination algorithm is proposed in this study. It is an iterative process in which, initially, the independent market operator (IMO) declares the interval electricity price for the period of concern. The generation companies (GenCos) would then provide the independent system operator (ISO) and the IMO with their own maintenance proposals; based on their own objectives and constraints. The ISO would evaluate the impact of the maintenance proposals on the reliability indices and assign some penalties/rewards to the GenCos; in proportion to their contributions in reliability index violation from a desirable level. On the other hand, the IMO would declare the new electricity prices, considering the new maintenance proposals. These two signals would be used by the GenCos to review and modify their maintenance proposals. The procedure is repeated until an equilibrium point is reached. For convergence assurance, a memory rate is introduced by which the GenCos earlier experiences in providing the ISO with the maintenance proposals are, somehow, taken into account. The capabilities of the proposed algorithm are assessed and evaluated on IEEE reliability test system.

An integrated model for market pricing of energy and ancillary services

Abstract An important issue in deregulated power systems is the provision and pricing of ancillary services. In this paper, a pool-based market solution is proposed, where prices and volumes of energy and ancillary services are calculated simultaneously, recognizing the strong interdependence between these commodities. Generators and consumers prepare bids in the form of a statement of their own profit maximization problem. These problems are solved hour for hour by an independent Market Operator, using a Lagrange Relaxation based procedure. Market prices for energy and ancillary services are obtained as the dual values of their respective constraints. It is shown how market prices are obtained in a 10-unit example, and how demand-side bidding for ancillary services affects these prices.