Investigating Wind Generation Investment Indices in Multi-Stage Planning

This paper presents a Multi-stage stochastic bilevel model for the expansion planning of Wind resources in power systems at a multi-stage horizon. In this paper, the power system consists of a combination of fossil fuel technologies and Wind resources for investment. Demand is characterized by a certain number of demand blocks. The uncertainty of demand for each this block (for each time period of the curve) is determined by the scenario. Afterwards, the suggested model is converted to a mathematical programming with some equilibrium constraints. Following that, after linearization, a mixed integer linear program is obtained. This framework is examined on the IEEE RTS 24-bus network. The obtained simulation results confirm that this model can be appropriately used as a means to analyze the behavior of investments in wind and thermal units

Generation expansion planning in electricity market considering uncertainty in load demand and presence of strategic GENCOs

This paper presents a new framework to study the generation capacity expansion in a multi-stage horizon in the presence of strategic generation companies (GENCOs). The proposed three-level model is a pool-based network-constrained electricity market that is presented under uncertainty in the predicted load demand modeled by the discrete Markov model. The first level includes decisions related to investment aimed to maximize the total profit of all GENCOs in the planning horizon, while the second level entails decisions related to investment aimed at maximizing the total profit of each GENCO. The third level consists of maximizing social welfare where the power market is cleared. The three-level optimization problem is converted to a one-level problem through an auxiliary mixed integer linear programming (MILP) using primal–dual transformation and Karush–Kuhn–Tucker (KKT) conditions. The efficiency of the proposed framework is examined on MAZANDARAN regional electric company (MREC) transmission network – a part of the Iranian interconnected power system. Simulation results confirm that the proposed framework could be a useful tool for analyzing the behaviour of investment in electricity markets in the presence of strategic GENCOs

Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO2 Emissions

According to the European Union Emissions Trading Scheme, energy system planners are encouraged to consider the effects of greenhouse gases such as CO 2 in their short-term and long-term planning. A decrease in the carbon emissions produced by the power plant will result in a tax decrease. In view of this, the Dynamic carbon-constrained Equilibrium programming equilibrium constraints (DCC-EPEC) Framework is suggested to explore the effects of distinct market models on generation development planning (GEP) on electricity markets over a multi-period horizon. The investment incentives included in our model are the firm contract and capacity payment. The investment issue, which is regarded as a set of dominant producers in the oligopolistic market, is developed as an EPEC optimization problem to reduce carbon emissions. In the suggested DCC-EPEC model, the sum of the carbon emission tax and true social welfare are assumed as the objective function. Investment decisions and the strategic behavior of producers are included at the first level so as to maximize the overall profit of the investor over the scheduling period. The second-level issue is market-clearing, which is resolved by an independent system operator (ISO) to maximize social welfare. A real power network, as a case study, is provided to assess the suggested carbon-constrained EPEC framework. Simulations indicate that firm contracts and capacity payments can initiate the capacity expansion of different technologies to improve the long-term stability of the electricity market

Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

Power grids include entities such as home-microgrids (H-MGs), consumers, and retailers, each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs. Therefore, there is the need for a smart structure to handle the new situation. This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources (DERs) and energy storage in H-MGs by considering the demand response (DR). In general, the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs. The upper-level problem is decomposed into a set of low-level market clearing problems. Both electricity and heat markets are simultaneously modeled in this paper. DERs, including wind turbines (WTs), combined heat and power (CHP) systems, electric boilers (EBs), electric heat pumps (EHPs), and electric energy storage systems, participate in the electricity markets. In addition, CHP systems, gas boilers (GBs), EBs, EHPs, solar thermal panels, and thermal energy storage systems participate in the heat market. Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources, but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price

Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO2 Emissions

According to the European Union Emissions Trading Scheme, energy system planners are encouraged to consider the effects of greenhouse gases such as CO 2 in their short-term and long-term planning. A decrease in the carbon emissions produced by the power plant will result in a tax decrease. In view of this, the Dynamic carbon-constrained Equilibrium programming equilibrium constraints (DCC-EPEC) Framework is suggested to explore the effects of distinct market models on generation development planning (GEP) on electricity markets over a multi-period horizon. The investment incentives included in our model are the firm contract and capacity payment. The investment issue, which is regarded as a set of dominant producers in the oligopolistic market, is developed as an EPEC optimization problem to reduce carbon emissions. In the suggested DCC-EPEC model, the sum of the carbon emission tax and true social welfare are assumed as the objective function. Investment decisions and the strategic behavior of producers are included at the first level so as to maximize the overall profit of the investor over the scheduling period. The second-level issue is market-clearing, which is resolved by an independent system operator (ISO) to maximize social welfare. A real power network, as a case study, is provided to assess the suggested carbon-constrained EPEC framework. Simulations indicate that firm contracts and capacity payments can initiate the capacity expansion of different technologies to improve the long-term stability of the electricity market

Demand Response Based on the Power Factor Considering Polynomial and Induction Motor loads

Demand Side Management (DSM) can bring numerous benefits to power systems, such as decreasing peak load demand, reshaping demand and enhancing system security. In order to achieve these goals, this paper proposes a two-stage model for DSM considering polynomial and induction motor loads. Induction motor includes refrigerator (RFER), dishwasher (DWSH), clothes washing machine (CWSH) and dryer style motor (DRYR) loads. The polynomial and RFER loads follow a continuous DSM while the DWSH, CWSH and DRYR loads follow a selective DSM. Considering both the active and the reactive demand response, this paper provides a new DR based on the power factor. Electrical appliances are further classified as responsive/controllable and non-responsive/uncontrollable devices on the basis of their distinct power consumption constraints. Here, all appliances' operation patterns are modeled through their functions in real systems; as a result, the polynomial load model and induction motor type loads are adopted to represent consumers' behavior. A stochastic Multi-Objective Optimal Network Operation (SMONO) framework is further proposed to solve the aforementioned DSM problem. The simulations conducted in a generic distribution network reveals that the proposed method can successfully reach an optimal trade-off between four objectives, namely, voltage security, power losses, customer costs, and peak demand

Security Constrained Two-Stage Model for CO2 Emission Reduction

This paper introduces an innovative method for implementing demand response (DR) to enable household appliance scheduling to minimize CO2 emissions and improve the voltage security in transmission networks. A new demand response (DR) based on the time-varying emission curve is proposed in this paper to reduce CO2 emissions. In addition to emission-based DRs, non-responsive loads are considered. On the other hand, load modeling is believed to be one of the significant parts of the power system studies so that inaccurate load models can lead to dramatically incorrect simulation outputs leading to an unfortunate event such as the 1983 Swedish blackout. DR is therefore applicable to a number of loads, including induction type motors as well as exponential loads. In addition, both active and reactive DRs are considered in this model. This paper introduces a new model called the Security Constraint Two-Stage Framework arising from the complexity of the problem. This model includes a large scale (LS) stage and a small scale (SS) stage set in which the SS stage uses the LS stage results as inputs. The proposed design is being implemented on the IEEE 300 bus power network to investigate the desired objectives

Reconsidering insulation coordination and simulation under the effect of pollution due to climate change

Climate change and air pollution have a direct impact on the performance and lifetime of insulating materials. In recent years, Mazandaran and Golestan provinces in Iran have witnessed a climate change and an increase in air pollution. This problem increases outages and losses in transmission and overhead distribution network. Moreover, traditional and empirical methods have been used for insulation coordination in past decades. As a result, research is needed on the regional power line isolation, revision of insulated surface design, and the number of insulators used. In this paper, a specialized software was used for checking existing lines in Mazandaran and Golestan Regional Power. Insulation computations and isolation studies using MATLAB software were used to present solutions for transmission and overhead distribution networks. This specialized software has the ability to calculate the required minimum air gap and the minimum creepage distance and the number of insulators with due attention to regional pollution, overvoltage due to switching and lightning, and power frequency