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In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on a genetic algorithm (GA) for GEP in the presence of wind power plants. Since it is desirable to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow the maximum reasonable amount of wind penetration in the network to be obtained. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.

In the field of sector integration, the expansion of district heating (DH) is traditionally discussed with regard to the efficient integration of renewable energy sources (RES) and excess heat. But does DH exclusively benefit from other sectors or does it offer advantages in return? So far, studies have investigated DH only as a closed system or determined intersectoral benefits in a highly aggregated approach. We use and expand an integrated generation and transmission expansion planning model to analyze how the flexibility of DH benefits the energy system and the power transmission grid in particular. First of all, the results confirm former investigations that show DH can be used for efficient RES integration. Total annual system cost can be decreased by expanding DH, due to low investment cost and added flexibility, especially from large-scale heat storage. The high short-term efficiency of heat storage—in combination with electric heating technologies—can be exploited to shift heat demand temporally and, using multiple distributed units, locally to solve electric grid congestion. Although it is unclear whether these results can be replicated in the real world, due to the aggregation and detail of the model, further research in this direction is justified.

This paper represents a generation expansion planning (GEP) in the restructured power systems. For decoupling generation expansion planning from transmission expansion planning, a new method as T-index is used. The Benders’ decomposition is implemented to reduce the complexity of problem. The Benders’ decomposition divides the main problem into two subproblems—the first subproblem is the master problem which wants to maximize profits of each GENCO (PBGEP), and the second subproblem is the security problem which wants to satisfy security network constraints (SCGEP). Also, the second subproblem is divided into several subproblems such as feasibility subproblem, security-constrained unit commitment subproblem, T-index subproblem and optimal operation subproblem. Outputs of this algorithm included the place, type, size, and timing for the adding of new units. Also, we calculate value of each GENCO’s profit and total profit. The value of the generated emission is one of the factors for generation expansion planning in this paper. This constraint is applied in GEP to reach allowable value for total generated emission. Finally, an application of the proposed method is discussed and concluded.

Investment on generation system and transmission network is an important issue in power systems, and investment reversibility closely depends on performing an optimal planning. In this regard, generation expansion planning (GEP) and transmission expansion planning (TEP) have been presented by researchers to manage an optimal planning on generation and transmission systems. In recent years, a large number of research works have been carried out on GEP and TEP. These problems have been investigated with different views, methods, constraints and objectives. The evaluation of researches in these fields and categorising their different aspects are necessary to manage further works. This study presents a comprehensive review of GEP and TEP problems from different aspects and views such as modelling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion, reactive power planning, demand-side management and so on. The review results provide a comprehensive background to find out further ideas in these fields.

The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The findings of the study show that, by considering life cycle impacts alongside cost, grid expansion solutions move towards greener alternatives because the benefits of decommissioning fossil-fuel-based generation outweigh the costs associated with deploying new generation resources. Furthermore, an equity-based approach ensures that socially vulnerable populations are less impacted by disaster-induced, long-duration power outages.

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools.

Increased deployment of variable renewable energy (VRE) has posed significant challenges to ensure reliable power system operations. As VRE penetration increases beyond 80%, the power system will require long duration energy storage and flexibility. Detailed uncertainty analysis, identifying challenges, and opportunities to provide sufficient flexibility will help to achieve smooth operations of power system networks during the scenario of high share of VRE sources. Hence, this paper presents a comprehensive overview of the power system flexibility (PSF). The intention of this review is to provide a wide spectrum of power system flexibility, PSF drivers, PSF resources, PSF provisions, methods used for assessment of flexibility and flexibility planning to the researchers, academicians, power system planners, and engineers working on the integration of VRE into the utility grid to achieve high share of these sources. More than 100 research papers on the basic concepts of PSF, drivers of the PSF, resources of PSF, requirement of the PSF, metrics used for assessment of the flexibility, methods and approaches used for measurement of flexibility level in network of the power system, and methods used for the PSF planning and flexibility provisions have been thoroughly reviewed and classified for quick reference considering different dimensions.

Traditionally, long-term investment planning models have been the apparent tool to analyse future developments in the energy sector. With the increasing penetration of renewable energy sources, however, the modelling of short-term operational issues becomes increasingly important in two respects: first, in relation to variability and second, with respect to uncertainty. A model that includes both may easily become intractable, while the negligence of variability and uncertainty may result in sub-optimal and/or unrealistic decision-making. This paper investigates methods for aggregating data and reducing model size to obtain tractable yet close-to-optimal investment planning decisions. The aim is to investigate whether short-term variability or uncertainty is more important and under which circumstances. In particular, we consider a generation expansion problem and compare various representations of short-term variability and uncertainty of demand and renewable supply. The main results are derived from a case study on the Danish power system. Our analysis shows that the inclusion of representative days is crucial for the feasibility and quality of long-term power planning decisions. In fact, we observe that short-term uncertainty can be ignored if a sufficient number of representative days is included.

Indonesia’s power generation roadmap aspires to achieve 23%, 28%, and 31% of power from renewable energy by 2025, 2038, and 2050, respectively. This study presents a technoeconomic analysis of Indonesia’s power generation development plans using the LEAP model in the post-COVID-19 period, with a focus on achieving the renewable target. In this study, four scenarios were modeled: business as usual (BAU), cost optimization (CO), national plan (NP), and zero-carbon (ZC). The BAU scenario is based on the PLN Electricity Business Plan 2019–2028, which does not include a target for renewable energy. The CO scenario aims to meet the renewable energy mandate at the lowest possible cost. The NP scenario aims to achieve renewable energy, with an additional natural gas target of 22% by 2025 and 25% by 2038. The ZC scenario aims to achieve 100% renewable energy by 2050 at the lowest possible cost. In comparison to the other scenarios, the BAU scenario has the highest total cost of power production, with a total of 180.51 billion USD by 2050. The CO scenario has the lowest total cost of production with a total of 89.21 billion USD; however, it may not be practical to implement.