Generation Expansion Planning Research Articles

Impact of Electricity Price Expectation in the Planning Period on the Evolution of Generation Expansion Planning in the Market Environment

With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market.

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China’s total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixed- integer linear programming optimization approach for GEP. This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China’s power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Generation Expansion Planning Considering Discrete Storage Model and Renewable Energy Uncertainty: A Bi-Interval Optimization Approach

Both discrete storage model (DSM) and continuous storage model (CSM) have been used in the power system planning literature. In this article, we conduct a sizing-error analysis for the use of CSM in generation expansion planning (GEP), which shows more reasonable storage sizing decisions are offered by the DSM in comparison to the CSM. However, when the DSM is considered in the context of interval optimization, the discrete status variables in mutually exclusive constraints and the strong temporal coupling in state-of-charge constraints create significant challenges. To tackle this, a tailored interval optimization approach is proposed to consider both DSM and renewable energy uncertainty in GEP. Our approach is proved to cover all worst cases in a given uncertainty set, meanwhile running in an iteration-free manner. Moreover, to reduce the conservativeness of investment decisions, a bi-interval policy is designed to achieve a better tradeoff between investment cost and system security.

A two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints

After proposing the carbon peaking and carbon neutrality target, China further proposed a series of specific carbon emission growth limit sub-targets. How to decarbonize the energy system to ensure the realization of the carbon growth limit sub-targets is a meaningful topic. At present, generation expansion planning of renewable energy in integrated energy systems has been well studied. However, few of the existing studies consider specific carbon emission growth targets. To address this research gap, a two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints is proposed in this paper, which takes into account multiple uncertainties. In this framework, the objective function is to minimize the total operation cost and wind turbine investment cost. The first stage is the decision-making level of the wind turbine capacity configuration scheme. The second stage is the optimal economic dispatching in the worst-case scenario, which is a bi-level problem of max-min form. Thus, the two-stage robust optimization framework constitutes a problem of min-max-min form, which is pretty hard to solve directly with a commercial solver. Therefore, a nested column-and-constraint generation algorithm is adopted and nested iterations are performed to solve the complex problem. Finally, case studies are carried out on a regional electric-gas integrated energy system. The MATLAB/YALMIP simulation platform with the Gurobi solver is used to verify the effectiveness and superiority of the proposed framework. Compared with other four cases, 5,000 Monte Carlo scheduling tests demonstrate that the proposed framework can ensure the system carbon emission to be controlled within a certain limit even in the worst scenario. Due to the consideration of multiple uncertainties, the proposed framework planning results are both robust and economical for investment. This study can provide theoretical support for the actual regional integrated energy system to achieve a certain carbon growth target.

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

This work addresses the internalization of externalities in energy decision making and in generation expansion planning (GEP). Although the linkage between externalities and energy is well recognized, the issue of the internalization in GEP models and from a sustainability perspective is still far from being fully explored. A critical literature review is presented, including scientific articles published in the period from 2011 to 2021 and selected from scientific databases according to a set of pre-defined keywords. The literature is vast and quite heterogeneous in the models and methods used to deal with these externalities, and therefore a categorization of these studies was attempted. This categorization was based on the methods used, the geographical scope, the externalities included in the planning model and the strategies for their inclusion. As a result, it was possible to perceive that most studies tend to focus on the internalization of externalities related to CO2 and equivalent emissions from a national perspective. Departing from the critical analysis, pathways for future research were presented, highlighting the need to improve the internalization of social externalities to overcome environmental and economic bias, and also highlighting the importance of recognizing regional specificities and development priorities.

Decarbonizing energy islands with flexibility-enabling planning: The case of Santiago, Cape Verde

The growing interest in fully decarbonizing worldwide energy systems requires abandoning traditional generation expansion planning in favour of other flexibility-enabling energy system planning tools allowing the integration of energy storage and sector coupling. Therefore, this paper proposes a mixed-integer linear programming formulation focused on enabling flexibility provision on integrated energy systems targeting independent sizing of power and energy capacities for simultaneous generation and storage expansion planning. The optimization problem minimizes investment, maintenance, operation and emissions costs over a 20 year horizon with hourly resolution. In this context, islands represent suitable study cases for the energy transition due to their exceptional renewable availability, and fast-paced development; despite being regions with extreme external dependence and isolation. In particular, the island of Santiago, Cape Verde is selected as study case given its existing targets regarding reaching 50 and 100% renewable shares in 2030 and 2040, its data availability, and the extreme seasonal variation in wind and solar resources. Four scenarios are considered. The first and second dismiss flexibility: business as usual (BAU), and another one aligned with the government goals (Green). Then, the other two exploit it: one equivalent to Green, FlexG, and a last one finding the Optimal. In addition, three energy demand growth levels are considered in order to reduce uncertainty, corresponding to 1, 3 and 5%. Last, a sensitivity analysis with three additional scenarios is performed to provide a thorough view of Cape Verde’s energy future. The results highlight the importance of flexibility exploitation which provides up to 85% savings and allows to decarbonize other sectors via electrification.

Stochastic Flexible Power System Expansion Planning, Based on the Demand Response Considering Consumption and Generation Uncertainties

This paper presents the generation and transmission expansion planning (GTEP) considering the switched capacitive banks (SCBs) allocation in the power system, including the demand response program (DRP). This scheme is based on the system flexibility. The objective function of the scheme minimizes the expected planning cost that is equaled to the summation of the total construction costs of the SCBs, the generation units (GUs) and the transmission lines (TLs), and the operating cost of the GUs. It is concerned with the AC power flow constraints, the planning-operation model of the mentioned elements, the DRP operation formulation, and the operating and flexibility limits of the network. In the following, the scenario-based stochastic programming is used to model the uncertainty parameters, such as the load and renewable power of wind farms. Then, the hybrid evolutionary algorithm, based on the combination of the crow search algorithm and the grey wolf optimizer, is used to determine the optimal point with the approximate unique solution. Finally, the scheme is applied on the transmission networks, the numerical results confirm the capabilities of the proposed scheme in simultaneously improving the flexibility, operation, and economic situation of the transmission network, so that the hybrid algorithm achieves the optimal solution in a shorter computation time, compared with the non-hybrid algorithms. This algorithm has a low standard deviation of about 92% in the final response. The proposed scheme with the optimal planning of the lines, sources, and capacitor banks, together with the optimal operation of the DRP succeeded in improving the energy loss and the voltage deviation by about 30–36% and 25–30%, compared with those of the power flow studies.

A New Decision-Making Strategy for Techno-Economic Assessment of Generation and Transmission Expansion Planning for Modern Power Systems

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.

Challenges of Generation and Transmission Expansion Planning Considering Power System Resilience and Provide Solutions

Since power systems are susceptible to damages induced by disastrous incidents, the assessment and improvement of system resilience are unavoidable as a new goal of planning and operation. On the other hand, the expansion of generation and transmission grids constitute an essential part of power system planning as it needs a huge budget. So, a primary concern of researchers has always been the optimal planning of power systems. This paper studies the emerging concept of resilience, its criteria, and indicators, how to enhance it, and the identification of its strengths and weaknesses. It also reviews the strategies recommended in the literature to improve power system resilience. The paper briefly reports the models for expansion plan analysis and the generation and transmission expansion planning (GTEP) tools with or without the target of resilience enhancement, which can be instrumental in future research and can be used to estimate the effectiveness of different tools. Furthermore, the paper discusses the planning problems, thereby opening the way for further work in future studies. Finally, the study presents the most eminent challenges of GTEP to accomplish better, resilient, and innovative plans to escalate power system resilience.

A unit commitment based-co-optimization of generation and transmission expansion planning to mitigate market power

• A unit-commitment-based generation and transmission expansion planning model is proposed. • The planning problem is subject to the market power's constraint. • The scenario-based model is structured as a mixed-integer bi-level programming (MIBP). • The lower-level problem computes the must-run generation (MRG) as a market index. • An exact algorithm using the reformulation-and-decomposition technique is utilized to attain the optimal solution. This paper aims to construct a novel strategy for coordinated generation and transmission expansion planning (CGTEP) considering market power. The integrated model constitutes bi-level programming from a centralized planning point of view. The upper level seeks to minimize the investment and operation cost subject to technical constraints, while the lower level's objective is to mitigate the market power. A stochastic unit commitment-based approach is employed at the upper level to find the least-cost solution and at the lower level to minimize the transmission-constrained market power index. Since the lower level contains integer variables, it is impossible to utilize the Karush-Kuhn-Tucker (KKT) conditions or duality theory (DT) to convert the bi-level problem into a single level. Hence, an exact algorithm using the reformulation-and-decomposition technique (RDT) is utilized to attain the optimal solution to this complex mixed-integer bi-level programming (MIBP) problem. The IEEE 118-bus electric power network is used for numerical studies. Results from the numerical studies signify the effectiveness of the presented model showing that a well-designated power system can significantly mitigate market power.

Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders

This paper proposes a combination of two optimization models for simultaneously determining strategic energy planning at both national and regional levels. The first model deals with a single-period energy mix where the electricity production configuration at a future date (e.g., 2050), based on the available generation sources, is optimally obtained. An optimization model, based on a non-linear goal programming method, is designed to ensure a mixed balance between national and regional goals. The desired energy mix configuration, which is the solution obtained by solving the first model, is then fed into the second model as the main data input. In the second model, a multiple-period generation expansion plan is designed which optimizes the energy transition over the time horizon from the present until the future planning date (2050). The model considers uncertain parameters, including the regional energy demand, fuel cost, and national peak load. A two-stage stochastic programming model is developed where the sample average approximation approach is used as a method of solution. The practical use of the proposed models has been assessed through application to the electricity generation system in China.

Application of hierarchical clustering on electricity demand of electric vehicles for GEP problems

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs' batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still a need to realistically solve the GEP problems and find the optimal investment decisions to tailor the incentives used by most governments to guide the market. There is also a need for a tool to analyze the effect of different charging power levels, charging policies, and penetration levels. The main goal of this paper is to provide a tool to determine realistic optimal investment plans and evaluate different cases. It is also very important to consider the stochastic nature of the electricity demand in GEP problems. We propose a scenario-based stochastic programming model to incorporate the variability in the electricity demand due to EV charging through a set of scenarios generated by Monte Carlo Simulation. The methodology starts with applying a simulation method to generate the electricity demand of EVs by considering all the possible factors affecting EVs' demand. Each iteration of this simulation represents a possible demand profile as a result of penetrating the EVs into the market. Using all these demand profiles in GEP is preferable, but it is not computationally efficient. Computational tractability is achieved by using the clustering technique to reduce the size of such scenarios. We propose clustering methods to select a representative set from the data sets generated by the simulation and integrate EVs into GEP problems by using the selected set. The GEP models are defined to represent EVs' demand explicitly and then solved to imply the benefit of the suggested methods. The results show that GEP models with a representative set produce more realistic solutions than the GEP models including only average EVs demand. To select representative sets, different clustering techniques and distance measurements are used and compared with respect to their performances. Two different methods are defined to choose the best number of clusters: the silhouette coefficient method and the

Robust Allocation of FACTS Devices in Coordinated Transmission and Generation Expansion Planning considering Renewable Resources and Demand Response Programs

Nowadays, the use of demand response programs (DRPs) in a variety of long-term and short-term planning problems has been explored. In this paper, a generation and transmission expansion planning (GTEP) model along with FACTS device allocation is presented. Furthermore, demand response programs are taken into account for more load flexibility. The proposed model is presented as a multi-objective minimizing problem considering emission, cost, and voltage security index. Furthermore, the conventional Pareto optimization is adopted using fuzzy weighted sum method (FWSM) to achieve a single-objective model. The final problem is constrained by equations of alternative current power flow, operation and voltage security limits, planning model of shunt FACTS devices, and operation of the DRP. Adaptive robust optimization (ARO) is used to reach suitable models for the active power of renewable resources and power consumption. As a main search algorithm, a hybrid combination of water cycle algorithm (WCA) and ant lion optimization (ALO) is proposed to find the optimum solution with a small standard deviation. The problem is tested on different standard IEEE systems, and the results validate the operation and network security improvement due to optimal location of FACTS devices. According to the results, the economic and environmental status of the network has also improved.

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient-constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching-learning-based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6-Bus and IEEE 89-Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

Optimal Loss of Load Expectation for Generation Expansion Planning Considering Fuel Unavailability

In generation expansion planning, reliability level is the key criterion to ensure enough generation above peak demand in case there are any generation outages. This reliability criterion must be appropriately optimized to provide a reliable generation system with a minimum generation cost. Currently, a method to determine an optimal reliability criterion is mainly focused on reserve margin, an accustomed criterion used by several generation utilities. However, Loss of Load Expectation (LOLE) is a more suitable reliability criterion for a generation system with a high proportion of renewable energy since it considers both the probabilistic characteristics of the generation system and the entire load’s profile. Moreover, it is also correlated with the reserve margin. Considering the current fuel supply situation, a probabilistic model based on Bayes’ Theorem is also proposed to incorporate fuel supply unavailability into the probabilistic criterion. This paper proposes a method for determining the optimal LOLE along with a model that incorporates fuel supply unavailability into consideration. This method is tested with Thailand’s Power Development Plan 2018 revision 1 to demonstrate numerical examples. It is found that the optimal LOLE of the test system is 0.7 day/year, or shifted to 0.55 day/year in the case of considering the fuel supply unavailability.

Generation expansion planning for Guizhou province based on the complementary characteristics of wind and solar

With the growth of wind power and solar power, power source clean transformation has become a feasible way for achieving the carbon peak target. As a hydropower resource-abundant province, Guizhou has already built Wujiang river cascade hydropower plants. This paper introduces the basic information of the Guizhou provincial power system and geometrical renewable resource distribution characteristics, especially the power source structure. Then, the wind and solar power complementary characteristics are analyzed by different copula conjunction functions. Then, the power structure transformation and generation expansion plan are provided with both carbon peak restriction and curtailment restrictions. At last, the future Guizhou provincial power system in 2029 operation simulation cases are provided, which can reduce the thermal power generation ratio to 42% and increase wind and solar power generation ratio to 8.5% and 13% approximately. The conclusion indicates that coal-fired power plant flexible reform and larger capacity energy storage installation effectively solve the larger power fluctuation and peak regulation demand with more renewable energy integration target.

A data‐driven inertia adequacy‐based approach for sustainable expansion planning in distributed generations‐penetrated power grids

Reduced rotational inertia in power grids due to increasing penetration levels of distributed generations (DGs) may lead to degraded performance of the traditional frequency control scheme. In response to this challenge, inertia adequacy has recently emerged as a new research area in modern power systems. Accordingly, an attempt is made in this paper to tackle inertia adequacy in the generation expansion planning (GEP) problem. This in turn helps to realize sustainable economic development. For this purpose, the inertia adequacy constraint is tied to the frequency stability metrics. In other words, minimum permissible inertia that satisfies frequency dynamic standards is defined as an inertia adequacy constraint in the GEP formulation. Further, an experimentally validated model of a cluster of DGs is employed to realize a penetrated power grid to deal with sustainable planning development. Finally, an iterative-based algorithm is proposed to expand the system with minimum cost and with the highest permissible penetration level of DGs. The effectiveness of the proposed algorithm is examined in the Garver test system.

Decarbonising Australia's National Electricity Market and the role of firm, low-carbon technologies

Many of the world's electricity systems are decarbonising, driven in most cases by the deployment of variable renewable energy technologies. This paper studies such systems using generation expansion planning in the form of a linear program to determine and understand least-cost pathways to deep decarbonisation of existing wholesale electricity markets over a 30-year horizon. This work extends previous generation expansion modelling with several, additional options that now enable the substitution of woody biomass for coal and biomethane for natural gas, the retrofit of carbon capture and storage to existing and new build fossil plant, and different constraints on variable renewable energy that differentiate between renewable resource adequacy, system security and economic performance. The most recent, independent, and authoritative technical and financial input data are also used. In all decarbonisation pathways studied, emission reductions are primarily driven by the replacement of the existing coal fleet with utility-scale solar photovoltaic and wind generation. Nonetheless, systematic application of these additional options gives rise to a wide range of different technologies for ‘firming’ renewable-rich, decarbonised systems, with batteries, pumped hydroelectric energy storage, fossil plant with co-fired biomass and biomethane, carbon capture and storage and nuclear small modular reactors all playing significant roles in different cases. This range of observed pathways is thought to have several implications for system planning, with variable renewable energy tending to be economically competitive up to an imposed technical limit, but with that limit being uncertain and therefore requiring continuous re-evaluation as our understanding evolves. The results of an extensive range of sensitivity studies also suggest that deep decarbonisation is achievable without large increases in costs for consumers, that we should embrace the optionality and competition between different technologies, and that stakeholders have significant choice about non-cost related impacts and preferences, which are manifold.

Muna-Buton System Generation Expansion Planning by Considering the Interconnection System

Electrical energy consumption has increased annually. It is in line with the fulfillment of electricity sales for the last five years (2013-2017) with a 5.1% growth per year. Muna and Buton are large islands in Southeast Sulawesi with a population of 360,682 and an area of 7,712.18 km2. Muna and Buton are two main cities in Southeast Sulawesi that are developing rapidly. Those two regions are relatively rich in natural potential, promoting local economic growth. The primary source of electricity for both regions is Buton. Current electricity consumption in Muna and Buton is relatively high, with a peak load of 37 MW primarily fulfilled by diesel power plants (pembangkit listrik tenaga diesel, PLTD) of 30.15 MW. The government's target to achieve a new renewable energy mix (NRE) of 23% in 2025 and 31% in 2050 is contrary to the situation of generations in Muna and Buton, which is currently still dominated by PLTD. The planning was conducted by looking at its effect on the cost of generation construction, reserve margin, energy mix, and total cost. The desired optimization value was achieved through several performed scenarios, i.e., an isolated or pre-interconnection scenario, assuming each system was separated, and an interconnection system, assuming that interconnection was performed in Muna and Buton system. The optimization method was carried out using mixed-integer linear programming (MILP) by employing the OSeMOSYS software platform. The optimization results show that the Muna-Buton generation expansion planning has been successfully carried out. Of the several performed scenarios, the scenario with the interconnection system can be selected as the best option. It is based on the total cost value and reduced generation costs of 1,073 IDR per kWh in 2022 and 1,362 IDR per kWh in 2047, with an average of 1,202 IDR per kWh.

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning