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

Abstract

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

“…This scenario also investigates the effect of the lower-level H-MGs forming a coalition on changes in the strategy of independent operations for the upper-level H-MG with a high priority. The mathematical model of this scenario is further clarified by the coalitional operation of H-MGs, as shown in (35) and (36). Equations (35) and (36) state the profit obtained through the coalitional operation of H-MGs at an upper level or a lower level.…”

“…This way, while considering an independent or a coalitional performance of all H-MGs, a central optimum performance decision-maker is included in an upper-level decision-maker to obtain energy optimum exchange among H-MGs and with retailers in an independent and a coalitional performance to maximize the profit of H-MGs [32] - [34]. The interaction between the two levels of the hierarchal structure of the game is a factor in the search for the optimal solution at both levels [8], [10], [35]. Consideration of the optimal scheduling of all H-MGs and DERs that exist in them in multiple H-MGs requires solving mathematical programming with equilibrium constraints (MPECs) that are equivalent to the bilevel problem [36].…”

Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System

“…However, due to the difficulty in transferring local energy resources, diesel generators continue to dominate the electricity production in Maluku, representing approximately 90.1% of the total generation in 2019 [3]. Considering the carbon dioxide emissions, generation expansion planning (GEP) must consider the emissions produced by generating units [6]. To avoid environmental problems and ensure energy security, the future energy mix should include a greater proportion of renewable energy [7].…”

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

“…The GEP optimal solution with carbon emission cost has been considered by many authors [37,38]. Currently, low-carbon technology is divided into two categories.…”