Investment in new generation capacity is considerably affected by issues such as regulatory-determined incentives, techno-economic uncertainties and financial risks. In addition, increased penetration of renewables such as wind capacities resulting in decreased market prices, leads to the augmented risk of investing in conventional generations such as thermal technologies, which may hamper generation adequacy. Thus, to meet regulatory goals for adequacy and environmental considerations, regulators should determine investment incentives considering targets for techno-environmental issues and generation investors’ risks. So, this paper presents a bi-level robust regulatory model, in which regulator in the first level designs incentives cons

Among many studies proposed to solve the DG placement problem, only a few papers have addressed this issue from DG owners' perspective, none of which has taken the important role of transmission costs allocated to DGs into account. In this paper, optimal placement of DG units in an optimal power flow based wholesale electricity market with the aim of maximizing DG owners' economic benefits is carried out, with specific consideration given to transmission costs allocated to DGs. The proposed approach takes both production and transmission cost components into account in the DG placement process, so that in order to achieve the most optimal size and site of DGs from their owners' point of view, the problem of optimal placement is formulated f

Inter-area generation expansion planning in a multi-period timescale with minimization of expansion cost and pollution is proposed in this paper. The expansion decision is made in a deregulated environment from the viewpoint of the strategic competitor. In this regard, the GENCO can expand its generation units accessing various generation technologies as candidate expansion plans in the main area. The main area consists of several buses while the adjacent areas are modeled as a single bus. The behavior of the other competitors is considered predictable by the strategic GENCO. In this study, the uncertainties of load, electricity price and wind speed are included. To confront them, rough neural network methodology is employed to forecast the

Nowadays, according to sustainable development targets in modern societies, electricity markets are gradually connecting together. In this way, in this paper, a multi-agent generation expansion planning in a joint multi-electricity market environment is proposed. The GENCO (Generation Company) agents, competing in a deregulated structure, try to obtain the most profitable investment and operation plan regarding the incentives/taxes that the regulatory sector legislates. The multi-area market assumption encourages the GENCOs to offer their energy blocks to adjacent electricity markets whenever the production capacity is higher than the local load. To confront the uncertainties such as wind speed, market price, and load demand, the Markov cha

As the share of variable renewable energy in power systems increases, incorporating the operational constraints in generation expansion planning has become necessary to address flexibility issues. Accordingly, to preserve chronology and to make the technically detailed planning model tractable, the model should be solved for some representative periods. Different approaches have been introduced to select representative periods; however, most of them ignore the importance of the net load. In this research, a method is proposed to include the extreme days with higher and lower levels of the net load in representative days by using self-organizing map clustering. Further, the impacts of the extreme days, with different weighting approaches, on

Environmental concerns and energy security issues have led to the rapid growth of renewable capacities in the power sector. With the increase in the penetration of renewables, conventional generations such as thermal technologies have faced the decreased market prices, reduced volume of sold energies and thus diminished revenues. The decrease in the interests for investment in conventional capacities resulting from revenue deficiency from one side and high penetration of non-dispatchable renewables such as wind and solar generations from the other side may hinder the generation adequacy. So, the investment in conventional and renewable capacities should be done in a coordinated manner to meet both adequacy and environmental goals. Thus, the

The accurate and reliable forecasting of wind power is of great importance for electrical systems’ control and operation. However, the intermittent nature of wind power generation implies a complicated forecasting framework. In this paper, a new hybrid model including three steps is proposed for point and probabilistic forecasting of wind power. Within the first step, by using data preprocessing methods, proposed weighted Extreme Learning Machine (ELM) by Mutual Information, and bootstrap approach, point forecasting and variance of the model uncertainties are estimated. In the second step, by employing ELM, bootstrap approach, and an ensemble structure, the noise variance is calculated. During the final step, to improve the results of the

This paper presents a cooperative bidding model for energy, reserve, and flexiramp providing by a group of suppliers. Procurement problems in electricity markets have been analysed through Stackelberg concept and modelled via bi-level programming. However, previous bi-level models have captured the game only in the context of the day-ahead or real-time market. In this paper, a stochastic two-stage multi-objective bi-level model for procurement problem in the dayahead and balancing markets is proposed; in which the Stackelberg game is simultaneously considered in these markets. In each stage of the proposed model, multiple decision-makers (DMs) including conventional and wind generating units as well as a demand response (DR) aggregator act

Curtailable loads (CLs) are regarded as one of the last lines of defence to maintain the security and stability of power systems. Nevertheless, if organised carefully, they can also be exploited as preventive security procurement measures for power systems. This is especially true for heavily loaded power systems in which the normal control schemes are already exhausted. In this study, a new power market approach is proposed to utilise CLs’ potential in ensuring voltage stability margin (VSM) for heavily-loaded power systems. The proposed method aims to provide a mechanism to engage CLs as active players in the power market, and hence, minimise the disruptive effect of the load curtailment. Next, CLs participation in the power market is u

In this paper, a novel trilevel programming model for energy, reserve, and flexiramp procurement problem in the day‐ahead market is proposed. The conventional and wind generating units are the strategic decision makers (DMs) in the upper level, which maximize their profit. In the middle level, a large industry and a demand response (DR) aggregator maximize their profit according to the market's status. Finally, the independent system operator (ISO) minimizes its total cost in the lower level. To solve the proposed trilevel model, a fuzzy max‐min technique for multilevel programming problem (MLPP) is proposed. The proposed model is applied to the 24‐bus IEEE test system to demonstrate the benefits of implementing the proposed model in

Due to economic reasons, only way for demand side management resources participation in capacity market is their participation by load serving entities, energy service companies, or retailers. To achieve the optimal participation of retailers in the capacity market, it is crucial to model the effective parameters in retailers’ behavior model and how they trade with the independent system operator and the consumers. So a novel method is proposed in this paper for modeling retailers’ behavior, considering demand side management resources availability for their participation in long term commitments. The capacity product of the retailer and its behavior are comprehensively modeled to trade in the capacity market. The competition of retail

Generation investors face uncertainties that can have a considerable effect on the profitability and, thus, on the generation investment plan. To hedge the risks associated with uncertainties in generation investment decision-making, this paper proposes a two-stage bi-level robust model for application in risk-averse price-making generation investment considering participation in the spot electricity market. The investor is immunized in the model against worst-case realization of uncertainties, such as future demand and power produced by non-dispatchable generation units. The level of risk-aversion in the proposed model is controlled using an uncertainty budget. The resulting mixed-integer linear programming model is solved using Benders de

virtually, all decisions in financial markets are made in the presence of uncertainty. Stochastic dominance is a well-known concept that is broadly implemented for decision making under uncertainty. Thanks to the advantages of this approach, a decision maker is able to exploit available information related to uncertainties with stochastic nature. In this paper, to cope with the uncertainties in the procedure of decision making for forward contracts in an electricity market, a modified stochastic dominance approach is proposed. In the suggested framework, instead of considering a single value for desirable goal in each scenario of benchmark, a profit interval has been allocated to reflect the economic targets of decision maker. In the next s