Transmission Expansion Planning Research Articles

Robust transmission expansion planning model considering multiple uncertainties and active load

Uncertainty must be well addressed in transmission expansion planning (TEP) problem, and it significantly affects the reliability and cost-effectiveness of power systems. Owing to the complex operating environment of power systems, it is crucial to consider different types of uncertainties during the planning stage. In this paper, a robust TEP model is proposed by considering multiple uncertainties and active load. Specifically, in this model, the uncertainties of wind power output and contingency probability are considered simultaneously. The uncertainties are described by scenario and interval, and the Benders decomposition technique is applied to solve the model. The feasibility and effectiveness of the proposed model are illustrated using the IEEE RTS and IEEE 118-node systems.

Transmission Expansion Planning for the Optimization of Renewable Energy Integration in the Sulawesi Electricity System

In order to meet the growth in demand and the renewable energy integration target, the Sulawesi Electricity System (SES) should be expanded. Currently, the SES is divided into two systems, namely South and North Sulawesi. These two systems have different characteristics, such as the system size, reliability and operational cost. North Sulawesi is smaller, weaker and more expensive than the South System. In order to improve the system reliability, generation and transmission expansion planning should be executed simultaneously to meet the economical investment cost and satisfy the reliability standard. For this purpose, the necessity of a backbone system with a higher voltage level than the existing 150 kV system should be considered, including the interconnection option between the two existing systems in Sulawesi. A methodology based on economic and technical criteria was developed to determine the backbone voltage level. Two voltage level options were considered, namely 275 and 500 kV. Several criteria were considered in order to determine the backbone voltage level, including the economic voltage, the line-loading limit, N-1 contingency, short circuiting, transient stability, voltage stability and small signal stability. The backbone voltage candidates should fulfil all of the criteria. The simulation was conducted in base- and high-demand scenarios, considering the fluctuation of future economic growth. The load flow and the dynamic analysis were simulated in a DIgSILENT Power Factory environment. The results showed that the 275 kV backbone, which was built in 2022, would violate three criteria: the economic voltage, N-1 contingency and voltage stability. On the other hand, the 500 kV backbone voltage level fulfills all of the criteria. The result of this research was considered in the electricity planning documents in Sulawesi. From a technical and economical perspective, the 500 kV backbone was chosen for the SES.

Tri-level coordinated transmission and electrical energy storage systems expansion planning under physical intentional attacks

In this paper, a model for tri-level coordinated expansion planning of transmission and electrical energy storage systems (ESSs) is proposed to decrease the destructive effects of physical intentional attacks. In order to implement the actions of the system planner (SP), attacker, and system operator (SO), the optimization problem is modeled as a planner-attacker-defender problem. In the first level, the SP determines the optimal coordinated expansion planning of transmission networks and ESSs. In the second level, having enough knowledge about the network, the attacker wants to inflict the maximum damage. In the third level, the SO endeavors to minimize the damage to the network by exploiting correcting actions, such as load shedding and generators and ESSs re-dispatch. To confront the mathematical complexity of the presented model, we propose a mathematical model to convert the tri-level problem to a single-level mixed integer linear programming problem. The model is implemented on Garver and modified IEEE 30-bus test systems, and numerical results are provided for various case studies. The obtained numerical results are compared by literature. The numerical results show the effectiveness of the model to reduce power systems vulnerability.

Hybrid transmission expansion planning and reactive power planning considering the real network uncertainties

AbstractConventional transmission expansion planning (TEP) has evolved with the increasing influence of distributed generations (DGs) and electric vehicles (EVs). This increases uncertainty in power systems. On the other hand, reactive power plays a key role in voltage stability, so modeling reactive power planning in TEP is very important. The amount of reactive power is a determining factor regarding the stability of the network voltage and in case of shortage, occurrence of some events may endanger the voltage stability. This research aims at proposing a hybrid TEP and reactive power planning for the first time. Furthermore, the impact of wind, PV power plants, and EVs, as the common sources of uncertainties, are included. The ultimate goal of HTEP‐RPP is to find the transmission line candidates that meet the reactive power requirements. The simulation case studies on IEEE 24‐ and 118‐ buses confirm that the TEP based on DCPF may lead to some unreliable operating conditions due to violating the voltage constraints. In this case, the HTEP‐RPP results, by applying the imperialist competitive algorithm (ICA), are more comprehensive and effective than the results obtained by gravitational search algorithm (GSA), for the studied problem or even the popular TEP solutions.

An integrated transmission expansion planning and battery storage systems placement - A security and reliability perspective

This paper presents a new mathematical approach for co-planning of transmission expansion planning and battery storage systems placement. An optimal placement of grid-scaled battery storage systems can help transmission systems in congestion managements and improve power system security. In addition, battery storage systems can increase power system reliability in contingency conditions. In this paper a security and reliability viewpoint is implemented for the simultaneous transmission expansion planning and the optimal placement of battery storage systems. The N-1 security constraints are applied to the expansion planning model. The reliability indices evaluated from second order outages are also involved in the decision making process. The Benders’ decomposition approach is used to reduce the computational burden of the proposed model and make it applicable for large scaled power systems. The IEEE 24-bus test system is used to evaluate the applicability of the proposed method.

Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems

With the increasing penetration of renewable generating units, especially in remote areas not well connected with load demand, there are growing interests to co-optimize generation and transmission expansion planning (GTEP) in power systems. Due to the volatility in renewable generation, a planner needs to include the operating decisions into the planning model to guarantee feasibility. However, solving the GTEP problem with hourly operating decisions throughout the planning horizon is computationally intractable. Therefore, we propose several spatial and temporal simplifications to the problem. Built on the generation expansion planning (GEP) formulation of Lara et al. (2018), we propose a mixed-integer linear programming formulation for the GTEP problem. Three different formulations, i.e., a big-M formulation, a hull formulation, and an alternative big-M formulation, are reported for transmission expansion. We theoretically compare the tightness of the LP relaxations of the three formulations. The proposed MILP GTEP model typically involves millions or tens of millions of variables, which makes the model not directly solvable by the commercial solvers. To address this computational challenge, we propose a nested Benders decomposition algorithm and a tailored Benders decomposition algorithm that exploit the structure of the GTEP problem. Using a case study from Electric Reliability Council of Texas (ERCOT), we are able to show that the proposed tailored Benders decomposition outperforms the nested Benders decomposition. The coordination in the optimal generation and transmission expansion decisions from the ERCOT study implies that there is an additional value in solving GEP and TEP simultaneously.

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee. Metaheuristics can even be combined with classical optimization to try to extract the best of both worlds. There is a vast literature that tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, a genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth–flame optimization (MFO), exchange market algorithm (EMA), sine cosine algorithm (SCA) optimization and imperialistic competitive algorithm (ICA) are tested and compared. The algorithms are applied to the standard test systems of IEEE 24, and 118 buses. Results indicate that, although all metaheuristics are effective, they have diverging profiles in terms of computational time and finding optimal plans for TEP.

Multi‐agent decentralized microgrids planning considering long‐term demand response model

Demand-side management resources, such as demand response programs (DRPs) are useful options for the reduction of energy cost according to the viewpoint of department of energy. Utilizing these resources in long-term studies like generation and transmission expansion planning (GTEP) needs long-term modeling. In this article, the GTEP was developed considering long-term model of DRPs using a cooperative game theory approach to minimize the total cost of microgrids (MGs). A two-level decision-making method was used to develop this model. On the top level, investment decisions were cooperatively made by MGs and a stochastic chance-constrained mixed integer linear programming formulation was modeled for investment decisions with operational uncertainties on the bottom level. The self-sufficiency index was also considered that guarantees supply of sensitive loads in islanding mode. Effectiveness of the proposed model was shown by numerical studies in three cases where in Case 1, load of each smart MG (SMG) will be supplied using its own resources and trading with the retail market. In Case 2, SMGs can also trade with each other in non-cooperative (profitable) approach and in Case 3, SMGs can trade with each other in cooperative approach. The total cost of SMGs was decreased in Case 2 compared to the previous case by 2.62%, which was due to the use of other SMGs' resources to provide load. Also, the total cost of SMGs was decreased in Case 3 compared to the two previous cases (2.92% and 0.3% decrease compared to the Cases 1 and 2, respectively), which was due to cooperative approach taken by SMGs in this case. In this approach, SMGs provide each other's load without additional cost and try to minimize the total cost of all the SMGs.

Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique

Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique

A linearized transmission expansion planning model under N − 1 criterion for enhancing grid‐scale system flexibility via compressed air energy storage integration

The concept of flexibility is defined as the power systems’ ability to effectively respond to changes in power generation and demand profiles to maintain the supply–demand balance. However, the inherent flexibility margins required for successful operation have been recently challenged by the unprecedented arrival of uncertainties, driven by constantly changing demand, failure of conventional units, and the intermittent outputs of renewable energy sources (RES). Tackling these challenges, energy storage systems (ESS) as one important player of the new power grids can enhance the system flexibility. It, therefore, calls for an efficient planning procedure to ensure flexibility margins by considering ESS's role in modern power systems. This paper proposes a novel mixed integer linear programming (MILP) model for transmission expansion planning (TEP) framework taking into account the role of compressed air energy storage (CAES) integration on improvements in system flexibility. The proposed framework is housed with a quantitative metric of grid-scale system flexibility, while a new offline repetitive mechanism is suggested to account for the N − 1 reliability criterion. The model is applied to different test systems, where the numerical results demonstrate the impacts of CAES units on system flexibility, investment plans, and the total costs.

A hybrid resilient static power system expansion planning framework

A power system developed solely based on reliability metrics is not expected to appropriately respond to the severe events' effects. Also, reinforcement of existing components may be very expensive if practical. One of the measures taken to have the proper tools to deal with a severe event is to consider the resilience index during generation and transmission expansion planning (GTEP). Microgrids can also increase the power system's resilience and reduce planning costs with the resilience index. In this paper, a hybrid resilient static framework for GTEP is presented, determining the microgrid' optimal penetration rate. The proposed framework is a two-stage framework that, in the first stage, the GTEP problem is solved. In the second stage, the results of planning are examined by the Monte Carlo simulation (MCS) to calculate the expected demand not served (EDNS) as the representative of the resilience index. For reaching the desired resilience, investment costs in the planning stage should be increased and used as a constraint in the next step. This process is repeated for each predetermined microgrids' penetration rate. The effect of the event on the transmission lines is modeled using the notion of fragility curve. In this framework, there is also the ability to use reinforced lines during GTEP as a planning option. The simulations performed on the two sample networks show the effectiveness of the proposed method.

Multi-objective resilient-constrained generation and transmission expansion planning against natural disasters

In this paper, the resilient-constrained generation and transmission expansion planning (RCGTEP) considering natural disasters such as earthquake and flood is presented. The proposed model contains a two objective problem that the first objective function minimizes the construction and operation costs of resiliency sources (RSs) and, in the second objective function, the minimization of the expected energy not supplied (EENS) due to the outage of the system against the mentioned natural disasters is formulated. Also, the proposed model is constrained to linearized AC optimal power flow (AC-OPF) equations, the planning and operating of RSs constraints such as generation units, hardening transmission lines, parallel and series FACTS devices, resiliency constraints and power system angular stability against earthquakes and floods in the presence of critical and non-critical loads. In order to achieve the best compromise response, the RCGTEP single-objective problem is formulated based on the ε constraint-based Pareto optimization. In addition, this model has the uncertainty of RSs availability against natural disasters, so stochastic programming is used for RCGTEP. Then, the Benders decomposition (BD) method is used to solve the proposed problem and achieve the optimal solution in the shortest possible time. Finally, by implementing the proposed model on an IEEE standard transmission network, the numerical results confirm the capability of this method in improving the economic, operation, angular stability and resiliency indices of the power system simultaneously.

A coordinated scheme for transmission and distribution expansion planning: A Tri-level approach

In traditional power system planning problems, transmission and distribution networks are separated, which is not suitable for future power systems due to the rapid development of active distribution networks. This paper presents a hierarchy framework to optimize the transmission and distribution network expansion in a simultaneous manner. The proposed decentralized decision-making architecture forms a Tri-level structure where the first level is the transmission expansion planning (TEP) problem solved by the transmission system operator (TSO) as the leader, while the second level is the distribution expansion planning (DEP) problem solved by the distribution system operators (DSOs) as the second level followers. The third level problem is the optimal economic dispatch problem solved by the independent system operator (ISO) as the third level follower. The economic dispatch problem is replaced by its primal-dual (PD) formulation leading to a bilevel multi-follower mixed-integer linear programming (BMF-MILP) problem. Then, by using multi-parametric programming (MPP) method, the BMF-MILP problem is recast as several single level independent optimization problems. Numerical experiments are shown to corroborate the efficiency and tractability of the presented model. The results show the new coordinated transmission and distribution expansion planning (CTDEP) model provides additional flexibility and reduces the total cost.

The impact of development priorities on power system expansion planning in sub-Saharan Africa

Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.

Generation and transmission expansion planning for bulk renewable energy export considering transmission service cost allocation

This paper proposes a bulk renewable generation and transmission expansion planning (GTEP) applied to the Iran power system. The objective is to minimize the cost of investors who intend to invest in renewable power plants for electricity exports in high-potential countries. The costs include the generation and transmission investment cost (CAPEX), operation cost (OPEX), and transmission service cost (TSC). In this paper, three TSC allocation methods are proposed to be used, including methods based on annual MWh of consumption or generation, based on the peak value of consumption or generation, and based on the power flow of lines. The proposed optimization-based GTEP is examined through various cases. The effects of different parameters including the location of the neighbors to whom the power is exported, the location of the candidate buses for installing power plants, the neighbor's load shape, and the type of renewable power plant are discussed on the GTEP results.

Renewable Generation and Transmission Expansion Planning Coordination with Energy Storage System: A Flexibility Point of View

This paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software.

Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach

This paper proposes a distributionally robust optimization method for assessing economic benefits of transmission expansions with thyristor controlled series compensation devices. To accommodate the uncertainty of wind generations and electrical vehicle loads, a distributionally robust optimization method is proposed to use historical data instead of predefined distribution probabilities. In the proposed framework, firstly, a confidence set for unknown distributions are constructed based on the historical data. Then, based on worst-case distributions within a constructed confidence set, the optimal results of transmission expansion planning and allocation of thyristor controlled series compensation device are obtained. In order to take advantage of the flexible alternative current transmission devices (FACTS) in both economic and technical considerations, the optimal allocation of thyristor controlled series compensation is integrated into the transmission expansion planning. This requires AC-based transmission expansion planning model. AC power flow suffers from high computational complexity. To tackle this issue, the linearized AC power flow model is utilized. Furthermore, to solve the optimization problem, the combined technique including Bender's decomposition and cut-and-column are utilized. The numerical results on the Garver 6-bus and IEEE 118-bus systems demonstrate the effectiveness of the proposed method in comparison with scenario-based stochastic and robust optimization methods. Besides, the impacts of the thyristor controlled series compensation on total cost reduction has been investigated.

Climate‐aware generation and transmission expansion planning: A three‐stage robust optimization approach

In this paper, we propose a three-stage robust generation and transmission expansion planning model considering generation profiles of renewable energy sources (RES) affected by different long-term climate states. Essentially, we extend the broadly utilized two-stage modeling approach to properly consider partial information of climate states with conditional short-term scenarios of RES output and outages. The proposed model is formulated as a five-level optimization problem. The first level determines the optimal generation and transmission expansion plan under uncertainty in climate conditions, RES generation, and contingencies. Given the selected expansion plan, the second level identifies the most severe climate state. Following the decision-information hierarchy, in the third level, the system operator optimizes the generation schedule of energy and reserves under perfect information of the climate state, but yet under uncertainty in the RES generation and contingencies. Then, the fourth level identifies the worst-case combination of contingency and conditional short-term RES generation adjusted to the current climate condition. Finally, the fifth level determines the optimal redispatch of reserves to react against the worst-case RES generation and contingency scenario considering the uppermost decisions. Within this multi-level structure, the optimal investment plan considers a more realistic decision setting, where system operators adapt RES forecasts based on the observed climate conditions before planning the operational schedule. To solve the problem, a variant of the nested column-and-constraint-generation algorithm is proposed with global-optimality guarantee in a finite number of steps. A case study based on the Chilean system illustrates the applicability of the model in a realistic network.

A comparative analysis of generation and transmission expansion planning models for power loss minimization

In modern power systems, renewable energy resources are becoming more relevant due to their eco-friendly nature and sustainable electrification. Therefore, it is important to consider the effects of the renewable energy resources (RERs) on the generation expansion planning and transmission expansion planning (GTEP) simultaneously. The proper assessment of power losses is essential in order to adequately evaluate the consequence of renewable energy resources in power systems. This paper presents multi-objective optimization problems that are solved using DC, modified DC and AC modelling methods in order to accurately and efficiently compute electrical losses and its impacts on the expansion planning procedure. This paper develops a Mixed Integer Non-linear Programming (MINLP) problem and was solved using CONOPT and CPLEX solvers embedded in Algebraic Modelling Language. The proposed methods are evaluated and validated on three case studies: IEEE Garver’s 6 bus system, IEEE 24 bus test system and a real-world Nigeria Power System. A comparative analysis of the three modelling approaches and a sensitivity analysis of the simulation results indicate that AC solution method provide an accurate computation of power losses and efficient optimal planning strategy as compared to the modified DC method.