Generation Expansion Planning Research Articles

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

AbstractIn power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open‐loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed‐loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long‐term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization‐based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three‐layer closed‐loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large‐scale renewable integration (XJTU‐ROTS) demonstrate the effectiveness of our proposed framework.

Generation Expansion Planning for Solvent-Storaged Carbon Capture Power Plants Considering the Internalization of the Carbon Emission Effects

Carbon capture power plants (CCPPs) can effectively eliminate the carbon-locking effect of coal-fired power generation systems, which constitute one of the essential technical pathways to achieve low-carbon transformation of the power structure. Capable of decoupling CO2 absorption and separation in terms of time, CCPPs equipped with solvent storage have stronger operational flexibility while promoting the realization of carbon emission reduction targets. Therefore, this study focuses on the generation expansion planning of power systems containing solvent-storaged carbon capture power plants (SSCCPPs). First, a mathematical model is proposed for SSCCPPs considering new operation characteristics of power generation-carbon capture. Second, the per kilowatt-hour carbon emission coefficient (kWh-CEC) of various power plants is analyzed by life cycle assessment (LCA), and a bilateral stepped carbon trading model is established to quantify power plants' whole life cycle carbon emission effects. Third, a generation expansion planning model considering the internalization of carbon emission effects is developed to explore the benefits of planning SSCCPPs in wind-coal intensive power systems and multi-energy integrated systems. The simulation results on the modified IEEE RTS-96 system and IEEE HRP-38 system have validated the economic and low-carbon performance of planning SSCCPPs in the system.

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Low-cost and flexible generation expansion planning with localization of power plants in a test bus system

ABSTRACT Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow Optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a Chimp Optimization Algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.

A generation and transmission expansion planning model for the electricity market with decarbonization policies

Globally, the power sector must undergo a profound transition to achieve the decarbonization development targets. Various roadmaps are implemented, but only from a macro perspective, lacking the consideration of the electricity market rules. In this paper, we develop and present a market-driven generation and transmission expansion planning (MGTEP) model considering the effectiveness of the electricity market. Specifically, generation and transmission companies incorporate hourly market trading and annual capacity investment into strategic decisions to maximize their profits, with the supply function equilibrium model to analyze bidding behaviors. An equivalent quadratic programming formulation is deployed to solve the trilevel MGTEP model. Meanwhile, the MGTEP model is coupled with decarbonization policies to support the state and federal government in assessing energy transition strategies. We implement the MGTEP model with carbon emission allowance and carbon tax policies for the southern China electricity market to achieve carbon peaking by 2030. Carbon emission allowance adopts an intensity-based cap based on generation companies' historical output. The case study results show that 50 % carbon emission allowance or 400 CNY/t carbon tax is required but with several drawbacks, including unsatisfactory decarbonization effect, excessive economic sacrifice, etc. Finally, the case study is extended to dual-track policies with different combinations of policies. An optimal combination is 70 % carbon emission allowance and 160 CNY/t carbon tax. In this case, the power sector's carbon dioxide emissions and electricity prices in the southern China electricity market would increase to 554.6 Mt and 864.34 CNY/MWh in 2030, respectively, along with a carbon price of 850 CNY/t.

Generation Expansion Planning considering environmental impact and sustainable development for an Indian state using the LEAP platform

GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose.

Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

Water and emission constrained generation expansion planning for Iran power system

This paper analyzes the impact of water and emission limitations on expansion planning of thermal power plants (TPPs), in Iran. TPPs need considerable amount of water for their cooling system. In Iran, water crisis of recent years has made it impossible to operate existing steam power plants in some areas, during summer season. This paper considers water availability and cost in generation expansion planning (GEP) of Iran. There are different types of water resources such as rivers, dams, treated wastewater, seawater, seawater transmission line, saline groundwater, and groundwater. Considering available capacity of water resources in each area and sorting their extraction cost in ascending order, some water consumption cost curves are derived. For this purpose, data of about 200 water resources located in different regions of Iran are analyzed. Provided GEP model includes a 15-years planning horizon and analyzes several scenarios of water availability. Although, in low water price scenarios, air temperature and height above sea level have more impact on placement of TPPs, it is different for high water price scenarios. Also, the effect of emission-related policies on GEP decisions is analyzed by considering various fuel types of TPPs, penalties for emissions, and global restrictions on greenhouse gas production.

Stochastic financial analysis of diesel generation extension vs investment in hybrid photovoltaic-diesel-battery in a microgrid in the Amazon indigenous community

Due to environmental, operational, and regional restrictions, Brazil still has a lot of communities that do not have an integrated electricity supply, however, the government aims to promote universal access to electricity in the country. In this way, diesel oil generators are the main electricity source for these isolated indigenous communities, but in the generation expansion planning, renewable energy sources are an important way to promote a more sustainable generation in these Amazon regions. This article aims to present an economic analysis based on a Cost-Effectiveness Analysis and Cost-Benefit Analysis, using the Monte Carlo Simulation that compares the extension of the diesel capacity versus a PV-diesel-battery (PVDB) system in the Maruwai indigenous community that is located in the state of Roraima (Brazil). The photovoltaic system and the battery storage system are considered an alternative instead of repowering the electricity supply of the increase in on-site diesel generator operation. The results show that the diesel breakeven price is far below the current diesel oil spot price, which indicates that the hybrid system with photovoltaic cells and batteries is more economically feasible. In turn, the stochastic analysis, the results indicate that the hybrid PVDB system is economically attractive, but needs political incentives to mitigate the uncertainties about the average return for the investor.

Incorporating externalities when optimising power system expansion plans

Power system expansion planning has traditionally been conducted in two phases: optimisation of the future generation system to satisfy forecast demands, followed by that of a complementary transmission system. However, it is now computationally realistic to optimise both aspects simultaneously, even for large and complex systems. At the same time, the range of candidate generation plant types to be considered has widened considerably, with the technical and economic characteristics of wind, solar and biomass plants now having to be compared with those of conventional thermal and hydro plants. It is also becoming increasingly important to take explicit account, within the planning process, of the so-called ‘externalities’ such as emission limits, budget constraints and demand-management measures, in addition to the environmental costs and secondary benefits associated with individual projects. This paper describes the results of a study to demonstrate the feasibility of explicitly considering a range of externalities when mathematically optimising integrated generation and transmission system expansion plans, using the Vietnamese power system as an example.

A Wasserstein Distance-Based Distributionally Robust Chance-Constrained Clustered Generation Expansion Planning Considering Flexible Resource Investments

This paper proposes a distributionally robust chance-constrained (DRCC) model for the clustered generation expansion planning (CGEP) of power systems. The proposed two-stage model minimizes the first-stage total cost along with the second-stage expected penalty cost with the worst-case probability distribution of renewable energy generation. A unit commitment model with flexibility constraints is embedded into the planning model, and the uncertainty is modeled via a Wasserstein distance (WD)-based ambiguity set. Demand-side resources (DSR) and concentrating solar power (CSP) plants are considered as candidates in the DRCC-CGEP model to enhance system flexibility, and the solution efficiency is improved through unit clustering. Furthermore, based on strong duality theory along with affine decision rule and conditional-value-at-risk approximation method, the proposed planning model is reformulated as a tractable mixed-integer linear programming problem. Numerical results show that the proposed WD-based DRCC-CGEP model is effective in improving the economics of the planning decisions while ensuring system reliability and maintaining computational efficiency.

Chance-constrained coordinated generation and transmission expansion planning considering demand response and high penetration of renewable energy

The integration of high-penetration renewable energy has led to a significant increase in the uncertainties of power systems, and coordinated generation and transmission expansion planning has attracted more and more attention. Demand response resources are regarded as a kind of virtual power supply, which can improve the flexibility of power systems and delay generation and transmission investment to a certain extent. This paper establishes a stochastic coordinated generation and transmission expansion planning model taking into account the impact of demand response programs. In particular, the demand response model describes the operating characteristics such as power, energy, reserve, response period, and response number in detail. Considering the difference in the degree of invocation of demand response programs by different power systems, the chance-constrained method is adopted, which can make the planning schemes flexibly adapt to different demand response levels and improve the practicability of the model. In the case study, numerical results of construction and operation indicate that demand response programs can play a role in replacing generation and transmission capacity. Specifically, although the response energy does not exceed 0.3% of the total load energy, the investment cost can be saved up to more than 20%. Moreover, the response level, flexibility parameters, response capacity, and the proportion of renewable energy will all affect the capacity substitution effect of demand response, which verifies the rationality and necessity of considering these factors in this paper.

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

Recuperation based multi-objective generation expansion planning for a real-world power system

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions.

Decision‐dependent distributionally robust integrated generation, transmission, and storage expansion planning: An enhanced Benders decomposition approach

AbstractIntegrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low‐carbon transition considering security constraints in the long run. A novel IGT&SP planning scheme is proposed to balance the planning cost, i.e. renewable energy sources (RESs) and energy storage systems, based on the distributionally ambiguity sets. A novel decision‐dependent ambiguity set is proposed to capture the relation between the uncertainties of RES output and long‐term planning. A two‐stage risk‐averse distributionally robust optimization is formulated, where the RESs, energy storage systems, and transmission line expansion are optimized in the first stage and a unit commitment problem is proposed in the second‐stage optimization to assess the performance of the expanded system. This problem is reformulated into a two‐stage optimization problem with complete mixed‐integer recourse, where the state variable is binary. A novel enhanced Benders decomposition algorithm is proposed to solve the IGT&SEP efficiently, where the cutting planes are generated by a primal‐dual relaxation of the recourse problem. Simulations are conducted on the modified IEEE‐30 test system and modified IEEE‐118 test system. Compared with adjustable robust optimization and L1‐norm Wasserstein distance‐based distributionally robust optimization, numerical results verify the effectiveness of the proposed IGT&SP, together with the solution algorithm.

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

AbstractThis paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

Uncertainty and sensitivity analysis of generation expansion planning in hybrid nuclear and renewable energy system

In this study, generation expansion planning (GEP) is executed with the lowest cost and pollutant emission, highest efficiency, and lowest loss of load probability (LOLP). A hybrid energy system integrated with nuclear and renewable sources will be a potential candidate for a carbon-free energy network. Integration of nuclear and renewable energy tackles the demerits present when they operate individually. In this new approach, the expansion of modular reactors and renewable energy is discussed in the presence of uncertainties raised by load demand, load duration curve (LCD), energy carrier prices, power plant depreciation rates, weather conditions, and renewables output. Moreover, sensitivity analysis is fulfilled to find the best reactor expansion capacity. Firstly, all uncertainties are modeled by their probability density function (PDF). Then a modified Metropolis Markov chain Monte Carlo (MCMC) coupled with the Wilks method was implemented to predict the stochastic behavior of different uncertainty sources exactly so that reaching to 90% reliability of the final plan. Finally, the approach is integrated into WASP-IV software to provide the optimal number of sources with optimal cost and pollutants. The results indicate that the LOLP index in the last year of the study period is only 1.4%–2.5%, and the number of installed nuclear units are 3 SMART reactor per 2 years just by 10% of tolerance, owing to considering all the uncertainties in the expansion process after sensitivity analysis. Furthermore, the emission of pollutants is about zero.

Generation expansion planning with a renewable energy target and interconnection option: A case study of the Sulawesi region, Indonesia

Sulawesi is a strategic economic region in eastern Indonesia that is facing high growth in electricity demand. Two challenges need to be addressed to achieve its renewable energy (RE) target. The region comprises two unconnected electricity systems, and its RE resources are scattered geographically. It is therefore harnessing RE resources and interconnecting these two electricity systems can be an optimal scenario for generation expansion planning (GEP). This study aims to develop an open-source GEP model for the Sulawesi region as well as identify the optimal electricity development pathway while considering not only the need to achieve higher penetration of renewables and reduce CO2 emissions but also the option and associated benefits and costs of interconnecting the two distinct electricity systems. The results show that the optimal RE development pathway is the interconnection scenario, which ensures a lower generation cost with reduced emissions. This scenario reduces the cost by USD 258.8 million compared with the total cost of the isolated scenario and ensures the RE target of 31% by 2050. This study recommends that investment in hydro and wind energy should be simultaneously encouraged, and robust policies are required to support investments in RE sources, such as geothermal, biomass, and solar energy.