Transmission Expansion Planning Model Research Articles

A new adequacy evaluation method for transmission expansion planning problems

Transmission Expansion Planning (TEP) problem refers to proposing the optimal number and location of new transmission lines in order to satisfy operation conditions with less investment costs. Adequacy evaluation of trial solutions is essential to solve TEP problem. This evaluation procedure may require a considerable computational e ort; therefore, improvement of Adequacy Evaluation Methods (AEMs) is a key to achieve more ecient TEP solution algorithms. It is a common practice to employ a Linear Programming (LP) called DC operation to perform this evaluation, especially in cases that generation rescheduling is considered in TEP studies. In this paper, an AEM is proposed as an alternative with less computational e ort to perform adequacy evaluation. In fact, the proposed AEM is applicable in TEP problems considering generation rescheduling. The study is based on DC TEP model where electrical aspect of the problem is modeled based on DC power flow equations. To examine the performance of the proposed AEM, this evaluation method is employed in a meta-heuristic TEP solution algorithm. Such a combinatorial algorithm is then applied to di erent standard test systems as well as practical cases. The simulation results show a promising improvement caused by employment of the proposed AEM.

Impact of network payment schemes on transmission expansion planning with variable renewable generation

A large number of studies have dealt with the Transmission Expansion Planning (TEP) problem. However, few investigations have focused on analyzing the impacts of network payment schemes on network configuration and the benefits/losses distribution among the participants in electricity markets. In this paper, we propose a multi-annual transmission expansion planning model considering four different network payment schemes to finance the construction of new transmission lines, seeking to reduce the total system costs. Wind and solar power generation are included in the model taking into account their variability. The proposed models are reformulated as Mixed Integer Linear Programming (MILP) problems. We use seven performance metrics related with congestion, nodal prices and generator benefits, among others, to evaluate the effect of each payment scheme. A realistic case study based on the main power system in Chile is analyzed to illustrate the proposed models. It is shown that integrating line cost-recovering equations into the TEP model may result into a more realistic and less congested power network. Also, total system cost is highly related with transmission tariff discrimination. In that way, tariffs with high location dependence perform better in the case studied, the Chilean power system.

Flexible transmission expansion planning associated with large‐scale wind farms integration considering demand response

With increasing large-scale wind farms being integrated into the power grids, transmission expansion planning (TEP) increasingly requires more flexibility to account for the intermittency as well as other uncertainty factors involved in the process. In this study, a probabilistic TEP model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms. Both load forecast and wind power output uncertainties are considered in the proposed model. Other factors considered in the model include the forced outage rates of transmission lines and generators, and the wind speed correlation between wind farms. Moreover, the incentive-based demand response (IBDR) program is introduced as a non-network solution instead of the conventional network expansion approaches. The utilities will pay IBDR providers for their contributions to peak demand reduction. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The hierarchical Bender's decomposition algorithm integrated with Monte Carlo simulation is employed to solve the proposed model. Case studies are given using the Garver's six-bus system and the IEEE-reliability test system to show the effectiveness of the method.

Specialized differential evolution technique to solve the alternating current model based transmission expansion planning problem

Abstract Solving the Transmission Expansion Planning (TEP) problem using the Alternating Current (AC) network model is a recent important trend. This problem is extremely difficult to solve due to its combinatorial nature and the non-linear behavior presented by using the AC network model, and only very few research works had tackled it so far. In this work, an improved and specialized application of Differential Evolution (DE) to solve the TEP problem in its static form, using the AC model and taking into account reactive power compensation is presented. The main goal of this work is to make feasible the use of the AC network model for TEP. This approach improves some features of traditional differential evolution in its binomial version to be applied to solve the TEP problem. Some comparisons are performed using some other swarm based meta-heuristics in order to demonstrate the good results obtained. Test and realistic networks are used to present the results of this new approach.

IGDT-based multi-stage transmission expansion planning model incorporating optimal wind farm integration

SUMMARY In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information-gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors' strategies. The TEP model is formulated for the risk-averse and risk-seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi-stage time horizon. A genetic algorithm approach is employed to solve the bi-level IGDT-based optimization problem. Finally, this IGDT-based model is applied to the simplified Iranian 400-kV system, and the results are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

An Improved Network Model for Transmission Expansion Planning Considering Reactive Power and Network Losses

The expansion plan obtained from a DC model based transmission expansion planning (TEP) model could be problematic in the AC network because the DC model is potentially inaccurate. However, solving TEP problems using the AC model is still extremely challenging. The motivation for this work is to develop a less relaxed network model, based on which more realistic TEP solutions are obtained. The proposed TEP model includes a linear representation of reactive power, off-nominal bus voltage magnitudes and network losses. Binary variables are added to avoid fictitious losses. Garver's 6-bus system is used to compare the proposed TEP model with the existing models. An iterative approach for considering the N -1 criterion during the planning process is developed and demonstrated on the IEEE 118-bus system. Simulation results indicate that the proposed TEP model provides a better approximation to the AC network and is applicable to large power system planning problems.

Security-constrained transmission expansion planning: A stochastic multi-objective approach

This article presents a stochastic multi-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the ε-constraint method is used to solve the formulated problem. The N−1 contingency analysis is also considered for the proposed TEP problem.The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm.

Hybrid AC/DC Transmission Expansion Planning

This paper proposes a hybrid algorithm for the ac/dc transmission expansion planning (TEP). The stochastic simulation method would consider random outages of generating units and ac/dc transmission lines as well as load forecast errors. The mixed-integer linear programming problem is decomposed into a master planning problem with integer investment decision variables and subproblems which examine the feasibility of master planning solution and calculate the optimal operation schedule over the planning horizon. The independent system operator would utilize the proposed method to select the optimal set of ac/dc transmission lines for satisfying TEP criteria: supplying load forecasts, minimizing investment costs, and optimizing market operations. The proposed set of dc transmission system may use either current source converters or voltage source converters. Numerical examples illustrate the effectiveness of the proposed TEP model.

Flexible Transmission Network Planning Considering Distributed Generation Impacts

The restructuring of global power industries has introduced a number of challenges, such as conflicting planning objectives and increasing uncertainties, to transmission network planners. During the recent past, a number of distributed generation technologies also reached a stage allowing large-scale implementation, which will profoundly influence the power industry, as well as the practice of transmission network expansion. In the new market environment, new approaches are needed to meet the above challenges. In this paper, a market simulation-based method is employed to assess the economical attractiveness of different generation technologies, based on which future scenarios of generation expansion can be formed. A multi-objective optimization model for transmission expansion planning is then presented. A novel approach is proposed to select transmission expansion plans that are flexible given the uncertainties of generation expansion, system load, and other market variables. Comprehensive case studies will be conducted to investigate the performance of our approach. In addition, the proposed method will be employed to study the impacts of distributed generation on transmission expansion planning.

CHOPIN, a heuristic model for long term transmission expansion planning

This paper describes the long term transmission expansion planning model CHOPIN. In CHOPIN, the network expansion is formulated as the static optimization problem of minimizing the global annual cost of electricity production, which is obtained as the sum of the annualized network investment cost, the operation cost and the reliability cost. The solution method takes advantage of the natural decomposition between the investment and operation submodels. The investment submodel is solved by a new heuristic procedure that in practice has invariably yielded the optimal plan. At the operation level CHOPIN optimizes over a multiplicity of scenarios which are characterized by the demand, the hydraulicity and the availability of components. The network is represented by any one out of four options: DC load flow (DCLF), transportation model and two hybrid models. Any of these models may consider the ohmic losses. The model is very efficient computationally; this fact was verified on rest examples, as well as on the actual transmission expansion planning of the Spanish system. >