Power System Expansion Planning Research Articles

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

Towards CSP technology modeling in power system expansion planning

Power systems with a high level of renewable energy penetration face challenges due to the inherent properties of some renewable energy sources, such as their variability and uncertainty. These systems require reliable technologies capable of handling large ramps and flexibility needs. Concentrating Solar Power (CSP) has been identified as one solution, which has been researched and implemented in several countries. This paper presents a novel analysis and evaluation of the impacts of choosing an inadequate representation of CSP technology in power system planning tools. The current state of CSP in power system expansion planning in the literature is examined, providing a classification for the modeling of investment and operation of CSP plants. Based on a proposed analysis framework, different models and configurations for CSP plants are evaluated and applied to a 5-bus test system. One of the key findings is that the selected model highly impacts the computational effort (150% more processing time), the quality of the planning decision (14% more overall costs), and the total emissions (up to 52% more emissions). A storage-based model proves to be the most suitable option, considering the operation and investment costs, as well as computational burden. The selected model achieves a 1% error compared to the results obtained with a reference model, while also requiring 40% less computation effort. The proposed framework can be applied to other power system structures by choosing the best suited CSP modeling.

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.

Enhanced proportional integral controller for DSTATCOM with particle swarm optimization algorithm in power system

Power system expansion planning is supported by using distributed generation (DG) instead of installing new main power system generators. The portability and autonomous operation of the DG make it capable of integrating into the distribution system. Power electronics devices for home loads, mainly electric vehicles, mobile phones, and laptops, introduce harmonics and related power quality issues to the distribution system. This load creates power quality issues like an increase in total harmonic distortion (THD) and a reduction in power factor. This paper uses the DG to supply important power electronics devices for the mitigation of power quality problems in distributed systems with non-linear loads, such as electric vehicles, called the “distributed static compensator (DSTATCOM)”. Wind and photovoltaic (PV) generators supply power to a common DC source. To improve power quality D-STATCOM powered by PV and wind energy systems at the DC link is incorporated. The direct axis/quadrature axis method (DQ method) controls the real and reactive components of the power. Proportional integral (PI) and particle swarm optimization (PSO) based PI controller parameter estimation are developed, and results are compared for power quality performance. In terms of improving power quality, the PSO-based parameter estimate of the PI controller performs better than the traditional PI controller.<br /><div class="extension_reset_css AnnotateNet_widget AnnotateReviewNotesButton" style="display: none; z-index: 2147483644;" data-id="ExtensionWidget"><div class="extension_reset_css AnnotateNet_widget annotateclickable" style="z-index: 8; display: none;" title="Resize" data-id="Widget"> </div></div>

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.

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.

Risk-informed coordinated generation and transmission system expansion planning: A net-zero scenario of Switzerland in the European context

Power systems around the world are changing due to the rapid transformation of the generation mix. Unprecedented configurations of the power system may increase the exposure to failures, and thus, lower security of supply. At the same time, the secure development of the system cannot neglect cost considerations. This demands for adequate tools to assess and prepare for future scenarios of the energy transition. In this paper, a risk-informed approach for generation and transmission expansion planning is developed. The approach integrates cost-based generation and transmission expansion planning and risk-based transmission expansion planning. Interfacing these two models to perform coordinated generation and transmission expansion planning results in solutions that are cost-effective, account for the risk implications of the systemic changes, and guarantee system security. The risk-informed coordinated generation and transmission system expansion planning is performed for the Swiss power system which is modeled in full detail for the year 2050. To account for electricity exports and imports, the four surrounding countries are considered in an aggregated manner and their 2050 capacities are projected assuming a net-zero scenario. The results of the risk-informed coordinated expansion planning show that the pure cost-minimization approach applied to either generation or combined generation and transmission expansion planning does not necessarily lead to a reliable power supply in Switzerland. In fact, not accounting for reliability leads to expansion solutions that may incur up to 16 times higher demand not served compared to the reference year 2018. This highlights the importance of considering in-depth system security analyses in power system expansion planning.

Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model

This paper presents a novel long-term model for the joint expansion planning of power and hydrogen systems with short-term operational considerations. We propose linear reserve constraints with adjustable parameters within the planning model to address the reliability requirements from significant levels of variable renewable generation. These adjustable parameters are calibrated using an iterative training methodology that connects the long-term planning model with a short-term unit commitment model. A crucial aspect of our methodology is the iterative improvement of investment decisions through the feedback obtained by evaluating the operational performance of the investment decisions in different scenarios computed under the unit commitment model. Computational experiments in a case study of a large-scale Chilean power-hydrogen system show the effects of the proposed methodology on capacity expansion recommendations, where a correct balance between variable and flexible technologies is obtained. Moreover, when simulating the operation of the system for a full year, we observe that the proposed methodology generates an investment plan that achieves a lower total cost compared to other methodologies commonly used in the literature. Additional experiments in 100% renewable scenarios show how conventional methodologies reaches levels of not-supplied-energy between 2%–7%, while the proposed methodology achieves less than 1% in the same metric.

Optimal sizing of energy storage in generation expansion planning of new power system with high penetration of renewable energies

With the consumption of fossil fuels and the impact of the greenhouse effect, renewable energies are ushering in a huge development opportunity, thus the optimal configuration of energy storage is essential to cope with the intermittence and fluctuation of renewable energies. This paper establishes a mathematical model for optimal sizing of energy storage in generation expansion planning (GEP) of new power system with high penetration of renewable energies. The objective function is to achieve the lowest total cost of investment and operation under the comprehensive consideration of various generation technologies and energy storage technologies. Carbon peaking and carbon neutrality goals are transformed into constraints of the GEP model to achieve high penetration of renewable energies in a power system. And 8760h operation curve are adopted to deal with the intermittence and fluctuation of renewable energies and obtains a more reasonable and realistic GEP results. Meanwhile, the optimal sizing of energy storage is solved in GEP model by detailed operation optimization and constraints of penetration rate and curtailment rate of renewable energies. Finally, the solving flow chart of GEP model and flow chart of optimal sizing of energy storage are given and the validity of this GEP model is proved in case analysis. In addition, carbon peaking goal may achieve in advance by vigorously developing wind power and PV power and configuring appropriate energy storage.

A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution

The transmission lines are used for power distribution across large distances. Different parameters affect the power transmission efficiency, and the quality of service. Furthermore, the transmission system parameter estimation is crucial for power flow analysis, electric power system expansion planning, stability, dispatch, and economic analysis. This task is created by utilizing system identification techniques, and with the analytical method being the most commonly utilized methodology for acquiring transmission line data. However, to address an issue that simplifies the estimation these techniques have significant downsides — such as non-recursive parameter estimation and the accessibility of an appropriately transposed line. This paper presents a hybrid moth-flame optimization (MFO) and particle swarm optimization (PSO) for estimating transmission line parameters based on various scenarios and mathematical validation on different benchmark functions. The concepts of MFO and PSO are rationally integrated into this algorithm to overcome their limitations and improve their global search ability. Regarding solution quality and convergence speed the results show that the proposed hybrid algorithm performs better than the conventional PSO and original MFO.

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 two-way pre-installation assessment framework for microgrids under power systems expansion planning

Many remote areas remain off the grid and are disconnected from the mainstream power network due to congestion in the existing power networks and expensive expansion plans. In recent studies, microgrid installations appeared as one of the most feasible solutions for power system expansion. This research study develops a two-way (M to G), where the existing microgrid is integrated into the distribution network, and (G to M) where distribution system expansion recommends the installation of a microgrid. The proposed framework is based on three steps. The initial assessment is based on a fuzzy assessment tool utilizing social factors, geographic location, and accessibility followed by economic feasibility analyses. These analyses are based on HOMER software and a power flow study of the microgrid to assess the system’s reliability. The proposed framework is implemented on an islanded microgrid in a village in Pakistan. The acquired results have shown that the proposed framework recommends the existing microgrid expansion in M to G mode. The Net Present Cost (NPC) of the grid-connected system is reduced from $11 million to $0.7 million. The cost of energy is also reduced to $4.42 compared to the $65 of the islanded systems, while the payback period is only 1.2 years compared to that of the islanded microgrid which is 8.6 years.

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. The goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.

Modelling of renewable energy power plant controllers for steady-state studies using an extended power flow formulation

The increasing penetration of large-scale wind and photovoltaic plants has created new challenges for the secure and reliable operation of power systems. To face these challenges, these renewable plants are composed of numerous inverter-based resources that use centralized controllers to satisfy grid code requirements related to frequency and voltage control, both under dynamic and steady-state conditions. In this paper, an extended power flow formulation is proposed for the steady-state modelling of power plant controllers along with their power flow controls including those associated with droop control, suitable for any plant topology and number of regulation devices. The effectiveness of this modelling approach is validated against WECC generic models using dynamic simulations in PSS®E. For this purpose, a 13-bus 5-generator test system representing a 100-MW renewable power plant is studied. The accuracy of the introduced formulation is demonstrated since it presents absolute errors smaller than 0.2% between both fundamentally different methods. Additionally, the well-known IEEE RTS-96 test system is used to model 29 power plant controllers with 93 generators, thus showing the practicality of the proposed formulation. It can be envisaged that this timely tool is valuable for power system operation and expansion planning considering large-scale variable renewable energy power plants.

Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm

In this paper, a transmission expansion planning (TEP) model is proposed to guarantee the resilience of power systems and mitigate cascading failures’ impacts. The energy storage systems and fault current limiters’ planning models are integrated into the TEP problem to minimize cascading outages and comply with short-circuit current reliability constraints. Most studies in the literature adopt a single strategy to simulate power systems’ cascading failures that may not be enough to guarantee networks’ resilience. This work elaborates on two scenarios for initiating cascading failures to study the impact of various initiating events on the planned system’s strength and the projects required. The TEP problem is formulated as a non-linear, non-convex large-scale problem. To avoid linearization issues and enhance meta-heuristics performance, a hybridization of two meta-heuristic techniques, namely snake optimizer and sine cosine algorithm (SO-SCA), is proposed to solve the problem. Two hybridization strategies are suggested to improve the exploration and exploitation stages. Defining future loads growth is essential for TEP. Hence, a load forecasting technique based on SO-SCA is investigated and compared with some methods reported in the literature. The results obtained proved the efficiency of the proposed approach in predicting load growth. TEP’s calculations were carried out on the Garver and the IEEE 24-bus system. The results demonstrated the superiority of the hybrid SO-SCA in solving the TEP problem. Moreover, the projects required to expand networks differed according to the type of cascading failures’ initiating scenario.

Cascade-risk-informed transmission expansion planning of AC electric power systems

Latest standards for transmission system planning require investigation of the performance during the simultaneous loss of two elements and an assessment of the risk of cascading outages. Consequently, transmission system expansion must also be supported by in-depth security analyses. In this study, we propose a risk-informed transmission expansion planning method. At the core of the method is an AC based cascading failure model that simulates the power system responses after contingencies. The method aims to provide an optimal expansion plan striking the balance between the cost of expansion and the risk of systemic failures. Addressing this challenge requires a simulation based multi-objective optimization, which we tackle using a meta-heuristic algorithm. The tool developed for transmission expansion planning is applied to the IEEE 118-bus system and compared with state-of-the-art transmission expansion approaches. Remarkably, the proposed TEP procedure produces solutions that improve system security at comparable expansion costs.

On representative day selection for capacity expansion planning of power systems under extreme operating conditions

Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve planning horizons of several years, solving the fullspace models where the operating decisions corresponding to all the days is intractable. Therefore, some “representative days” are selected as a surrogate to the fullspace model. We present an input-based and a cost-based approach in combination with the k-means and the k-medoids clustering algorithms for representative day selection. The mathematical properties of the proposed algorithms are analyzed, including an approach to calculate the “optimality gap” of the investment decisions obtained from the representative day model to the fullspace model, and the relationship between the clustering error and the optimality gap. To capture the extreme operating conditions, two novel approaches, i.e., a “load shedding cost” approach and a “highest cost” approach, are proposed to identify the “extreme days”. We conclude with a case study based on the Electric Reliability Council of Texas (ERCOT) region, which compares the different approaches and the effects of adding the extreme days.

Coordinated expansion planning problem considering wind farms, energy storage systems and demand response

During the recent years, the power system has entered a new technological era. The trends associated with increased commitment to wind farms (WFs) and energy storage systems (ESSs) as well demand side flexibility require disruptive changes in the existing power system structures and procedures. Being at the heart of a paradigm shift from passive users of the grid to active prosumers, storage owners and demand responsive actors, this paper expresses a flexible coordinated power system expansion planning (CPSEP) while considering local WFs, ESSs and incentive-based demand response programs (DRPs). This model minimizes the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account. The proposed framework is firstly formulated by mixed integer non-linear programming (MINLP), then to have a well-handed optimization model it is converted to mixed-integer linear programming (MILP). Additionally, the uncertainties of load, energy price, maximum WF generation and availability/unavailability of the network equipment are included in the proposed model where the first three parameters are modeled based on the bounded uncertainty-based robust optimization (BURO), and the scenario-based stochastic programming (SBSP) is used to model the last uncertain parameter. Finally, the proposed method is examined on several test networks to assess the performance of the proposed framework for flexi-reliable transmission network operation and planning.