Expansion Planning Model Research Articles

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.

Spatial representation of renewable technologies in generation expansion planning models

The increasing share of intermittent Renewable Energy Sources (iRES) in the energy mix leads to a growing need for their accurate representation in energy system models. In this paper, we analyze how the results of generation planning models are impacted by the spatial representation used to model intermittent renewable technologies. It is shown that the spatial resolution, and to a lesser extent the spatial aggregation method used in a planning model, have a significant influence on the obtained system cost. The effect is quantified in a case study of European geographical scale, by varying the spatial resolution from highly resolved (hundreds of sites) to highly aggregated (1 site). A resolution-dependency of the system cost is observed under multiple renewable generation targets, and different renewable capacity restrictions. In doing so, we highlight the importance of implementing an appropriate capacity limit, especially when a high spatial resolution is used. Additionally, basic spatial aggregation methods -to process high resolution input data- are compared.

Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

This paper proposes a new optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. The model optimizes both investment decisions (e.g., size, location, and time to install, retire and decommission facilities) and hourly operation decisions (e.g., on/off status, operating capacity, and expected power output). It is also able to optimize both the main generation capacity and reserve capacity to improve power system reliability. The impact of operational strategies of generators (i.e., participating in electricity production vs. remaining as idle units during operation) on power system reliability is considered. Probability of equipment failures and capacity failure states are used to rigorously estimate the power system reliability depending on design and operation strategies. The optimization model is firstly formulated with Generalized Disjunctive Programming (GDP), which is then reformulated as a mixed-integer linear programming (MILP) model using the Hull relaxation. Two reliability-related penalties, such as downtime penalty and unmet demand (or load shedding) penalty, are included in the objective function to maximize reliability while minimizing the total net present cost. Furthermore, a bilevel decomposition with tailored cuts is developed to reduce computational times of the multi-scale optimization model. The effectiveness of the proposed model is shown by comparing it with the expansion planning model without explicitly considering reliability. We also show that the proposed bilevel decomposition is computationally efficient for solving large-scale problems with millions of variables and constraints through 5-years and 10-years planning case studies.

Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

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.

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.

An Expansion Planning Approach for Intelligent Grids with Speculative Parallelism

Smart grid planning is a standard method used to reduce the net loss of distribution networks and improve the economic efficiency of power systems. As the distribution networks expand, the scale is getting more complex, leading to low efficiency and a long planning time when using traditional methods to implement new routes. To solve this problem, this paper proposed a new approach for intelligent grid expansion planning with speculative particle swarm optimization. First, the expansion planning model for the smart grid is established based on particle swarm optimization from the classical control system. Secondly, the model is parallelized with the speculative parallelism technique to overcome the influence of the internal control dependency and get rid of the original processing order. Finally, the parallel model is implemented on a distributed computing platform to improve the planning efficiency for complex intelligent grids significantly. Experiments show that the proposed approach can improve the efficiency by about 40% in an Apache Spark cluster consisting of 20 nodes compared with the conventional ones. Moreover, the proposed method can also fully utilize the distributed cluster’s computing resources.

Research on Data-Driven Distribution Network Planning Method

With the development of the intelligent and interactive power system, the elements of distribution network planning continue to increase. The distribution network connects the transmission system and individuals, directly affects the individual’s power consumption experience, and is a key link in the power system. A reasonable planning scheme can not only improve the power supply capacity and reliability of the distribution network but also fully apply the data of each system in the distribution network to realize the optimal planning of the medium-voltage distribution network driven by data. Firstly, this paper constructs the CIM model and the distribution network topology model and establishes the wiring pattern recognition feature library. The network reconstruction and planning method research was carried out for the target line, and a typical operation scenario of the distribution network was generated. At the same time, based on the time period network loss index, the distribution network reconfiguration optimization model and distribution network expansion planning model are established, and the solution method of the distribution network reconstruction and expansion planning model is expounded. A reconstruction optimization scheme with the best overall network loss performance is in the network operation scenario. The experimental results of the final example show that based on the proposed time period network loss index, the overall operation loss of the distribution network in a period can be calculated more accurately, and the optimized planning scheme is more suitable for the load power consumption characteristics of the region, and the method has certain feasibility.

Transmission expansion planning for power grids considering resilience enhancement

During recent decades, blackout accidents caused by cascading failures worldwide raised concerns of power grid operators and researchers about enhancing power grid resilience to withstand disruptions. As an effective approach to identifying critical components and quantitatively measuring transmission networks’ weaknesses, vulnerability assessment (VA) is instrumental for power grid resilience enhancement. This paper proposes a transmission expansion planning (TEP) model incorporating the VA to improve the resilience of the grid planning scheme. Firstly, a structural vulnerability index (SVI) and an operative vulnerability index (OVI) are introduced for the VA of buses. The affinity propagation clustering technique is adopted to reduce the dimension of the large masses of SVI/OVI data. In particular, considering that the vulnerability of a power grid largely depends on the most vulnerable components rather than the rest, the overall vulnerability is evaluated through both the mean and equilibrium value of the clustered SVI/OVI data. In order to integrate expert opinions and objective factors of the clustered SVI/OVI data, the analytic hierarchy process (AHP)-entropy method is adopted to design the weights in the overall VA for a TEP scheme. Finally, as an inverse problem of the overall VA for a TEP scheme, a multi-objective TEP optimization model is proposed, which minimizes the overall vulnerability as one of the objectives for enhancing power grid resilience. Case studies based on the IEEE 39-bus system and two practical TEP cases in central and southwestern China demonstrate the effectiveness of the proposed overall VA method and the TEP optimization model, which can help planners make a trade-off between economic investments and resilience enhancement.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Regional trade and expansion planning (RTEP) model: Model for regional power sector integration in Africa

Regional power pools present a significant and potentially defining opportunity for African power systems to develop domestic energy resources, improve system reliability, and contribute to overall economic development. Hydropower is expected to play a significant role in many regional power pools in Africa. Feasible power transmission highways from Grand Inga in Democratic Republic of the Congo and the Grand Renaissance Dam in Ethiopia to other regions on the continent create the possibility of a pan-African electricity grid. However, in the medium and long-term, global climate change is expected to cause major variations in Africa's hydrological resources and it is not known how these changes may impact the value of regional power sector integration. This paper presents a model developed to study the value of different levels of regional integration in sub-Saharan Africa and how this value may change in the face of climate change. This work builds on previous studies by incorporating the ability to trade between different regional pools, co-optimization of generation and transmission, the ability to share reserves, and detailed simulation of the major hydro basins in Africa. Numerical results of the analysis will be presented in a parallel paper.

A techno-economic planning model for integrated generation and transmission expansion in modern power systems with renewables and energy storage using hybrid Runge Kutta-gradient-based optimization algorithm

This paper presents an integrated generation and transmission expansion planning (G&TEP) model embedding with energy storage systems (ESSs) to reduce G&TEP projects’ cost, enhance power system’s reliability, decrease carbon emission, and increase the penetration of renewable energy systems. ESSs are a key component of modern power grids for their ability to solve many challenges and problems. Electrical system operators and planners often resort to ESSs, which are considered promising technology and are sometimes the only economically viable way to address deficiencies in the planning phase, specifically in the presence of renewable energy sources. However, choosing the right ESS type to solve a problem is still a challenge and essential to cost-effectively integrating ESSs into power systems. In this work, a techno-economic planning model is formulated to decide on a suitable ESS type for the optimal configuration of two power systems, taking into account the technical and economic aspects of ESSs. Three long-term and seven medium-term types of ESSs were tested for this purpose. A hybrid scheme of Runge Kutta optimizer and gradient-based optimizer is applied to solve the problem. The proposed planning model is implemented on the known Garver system and a real system in Egypt (Egyptian west delta network). The numerical results found that pumped hydroelectric storage is the most effective type in achieving N-1 reliability constraints, increasing the use of RESs, and reducing the planning cost. ESSs enhanced systems’ security and reduced the total planning cost by 0.86%–2.35% for the Garver network. The results showed that using ESSs is necessary for some power systems, like the west delta network, to avoid rolling blackouts. The investment and operating costs of ESSs, in the presence of RESs, reach more than 50 % of the total planning cost in some case studies. However, ESSs, in the absence of RESs and reliability constraints, increase the planning cost by 5%–50%. Finally, the hybrid scheme proved its superiority in solving the proposed problem.

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.

Assessing Potential Scenarios for Achieving New and Renewable Energy Targets in Java-Bali Power System, Indonesia

Geographic circumstances, government policies, and power system characteristics face many countries struggling to achieve their new and renewable energy (NRE). In addition, one characteristic of renewable energy (RE) which cannot be moved is a severe problem for archipelagic countries like Indonesia in achieving their NRE targets. Therefore, this research creates a long-term open-source generation expansion planning (GEP) model that considers renewable energy integration between islands, government policies, and power system characteristics of Indonesia. The model proposes a high voltage direct current (HVDC) line to facilitate abundant energy transfer between islands. The research also included multiple scenario analyses based on the potential strategies that could realistically be applied. Based on the long-term GEP model results, possible alternative routes to achieving NRE targets are mapped and assessed by considering power system characteristics and national energy policies. Specifically, the Java-Bali system of Indonesia is employed as a case study to demonstrate the performance of the proposed long-term GEP model. The optimum planning to achieve the targets produces the generation cost of 7.05 cents USD/kWh and the CO2 emission reduction of 2,297 million tons of CO2.

Transmission expansion planning with optimal transmission switching considering uncertain n-k contingency and renewables

This paper presents a novel model for transmission expansion planning (TEP) with optimal transmission switching (OTS) considering uncertain N-k contingency, renewables and loads. In the proposed approach, ambiguity set and budget uncertainty set are utilized to simultaneously characterize three types of uncertainties in power system: (1) wind power output; (2) load demands; (3) the status and probability distribution of generation and transmission N-k contingency. Accordingly, the optimal expansion plan and OTS can be obtained through a three-level model to guarantee the safety and economy of power system in the worst-case of wind power, load demand and N-k contingency. A decomposition algorithm based on benders decomposition is developed to efficiently solve the model. Cases studies on the revised IEEE 30-bus test system demonstrate that (1) the optimal expansion plan can be greatly affected by the proposed uncertainty sets, (2) the total costs can be effectively reduced by considering optimal transmission switching and (3) the decomposition method can efficiently solve the proposed TEP model.

A MILP-based heuristic algorithm for transmission expansion planning problems

In the last years, a lot of effort was placed into approximated or relaxed models and heuristic and metaheuristic algorithms to solve complex problems, mainly with non-linear and non-convex natures, in a reasonable time. On one hand, approximated/relaxed mathematical models often provide convergence guarantees and allow the problem to be solved to global optimality. On the other hand, there is no guarantee that the optimal solution of the modified problem is even feasible in the original one. In contrast with that, the metaheuristic algorithms lack mathematical proof for optimality, but as the obtained solutions can be tested against the original problem, the feasibility can be ensured. In this sense, this work brings a new method combining exact solutions from a Mixed-Integer-Linear-Problem (MILP) Transmission Expansion Planning (TEP) model and stochastic solutions from metaheuristic algorithms to solve the non-linear and non-convex TEP problem. We identify the issues that came up with the linear approximations and metaheuristics procedures and we introduce a MILP-Based Heuristic (MBH) algorithm to overcome these issues. We demonstrate our method on a single-stage TEP with the RTS 24 nodes and on a multi-stage TEP with the IEEE 118 nodes test system. The AC TEP solution was obtained using Evolutionary Computation, while the DC TEP solution was obtained using a commercial solver. From the simulations results, the novel MBH method was able to reduce in 42% and in 85% the investment cost from an evolutionary computation solution for the single-stage and multi-stage TEP, respectively.

A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China

In this study, a coupled non-deterministic optimization and mixed-level factorial analysis model (NOMFA) is proposed for supporting power generation expansion planning. By integrating interval-parameter programming, multistage stochastic programming and mixed-level factorial analysis within a general system optimization framework, this model can not only help determine the optimized schemes for power generation and capacity expansion under various uncertainties, but also reflect dynamic variations of system conditions; moreover, it can take into account the effects of external interferences and their interactions on the system outputs in the decision-making process. A case study of Jing-Jin-Ji (JJJ) metropolitan region is provided to demonstrate the effectiveness of the proposed approach. The results indicate that although renewable technologies are becoming increasingly significant, electricity generated by fossil fuels would still dominateJJJ’s power systems during the planning horizon. It is also found that the import price of electricity is the most influential factor on both total system cost and total CO2 emissions; meanwhile, the interaction between the import price of electricity and the price of gas impacts CO2 emissions to a certain extent. It is expected that the modeling results will provide solid bases for formulating power generation expansion plans for JJJ Metropolitan Region.

Coordinated generation expansion planning & energy storage planning model of the IPP's participation in the electricity markets

Independent power producer (IPP) as a zero emitter resource can contribute to a carbon emission reduction of the power system. However, the disappearance of the power purchase agreements (PPAs) or their short-term extension jeopardizes the future operation of the IPPs. Therefore, this paper proposes an expansion planning model for an IPP in the absence of PPAs. To this end, this paper coordinates the IPP's generation expansion planning (GEP) with an expansion planning in energy storage units in a bilevel optimization framework for an IPP to assess its capacity expansion to obtain the maximum benefit. In the proposed bi-level model, the profit of an IPP (or aggregated IPPs) is maximized in the upper level to obtain the strategic investment decisions, while the electricity market clearing considering the upper-level solutions is executed at the lower-level. In order to gain the maximum profit, the energy storage either could execute energy arbitrage or store the energy from the corresponding IPP in the proposed expansion planning model. In order to overcome the complexity of the proposed model, the bi-level nonlinear problem is recast to a mixed-integer linear problem. Finally, the IEEE 24-bus RTS is utilized to evaluate the performance of the proposed model.

Expansion planning of electric vehicle charging stations considering the benefits of peak‐regulation frequency modulation

The China Energy Administration has issued policies to encourage energy storage to participate in the electric auxiliary service market, which will provide ideas for electric vehicle charging stations (EVCSs) to collectively participate in peak-regulation and frequency modulation (FM). Taking into account the benefits of EVs participating in peak-regulation and FM, this paper studies expansion planning for EVCSs. First, the orderly scheduling strategy of EVs under vehicle-to-grid (V2G) technology is established in this paper. And a peak regulation method is proposed to maximize the economic benefits of charging and discharging during peak and valley periods. Second, based on the temporal and spatial prediction model of EV charging demand, the expansion planning model of EVCSs is constructed considering the benefits of peak-regulation and FM. Aiming at minimizing the comprehensive annual social cost of charging stations, the expansion site selection and capacity scheme of EVCSs are obtained. Finally, taking the 24 nodes in Xiangzhou District of Zhuhai City as an example, the location and capacity of the final annual expansion plan are obtained. And the influence of main control parameters on the results of the whole model and method is analysed in detail, the feasibility and effectiveness of the whole model and method are verified.