Rubén Romero

A hierarchical decomposition approach for transmission network expansion planning

This paper presents a hierarchical decomposition approach for optimal transmission network expansion planning. A major difficulty in obtaining global optimal solutions for complex, real-life networks is due to the nonconvexity of the problem. Hierarchical decomposition has proved to be an efficient heuristic for coping with nonconvexity, as illustrated in the test results section of the paper. Significant reductions in investment costs have been obtained in some practical cases for which results are available in the literature. The current implementation of the hierarchical decomposition approach utilizes three different levels of network modeling: transportation models, hybrid models, and linearized power flow models. An initial solution is obtained for the simplest model (transportation model) and as one moves towards the final solution the algorithm successively switches to more accurate models. >

Transmission system expansion planning by simulated annealing

This paper presents a simulated annealing approach to the long term transmission expansion planning problem which is a hard, large scale combinatorial problem. The proposed approach has been compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure. Tests have been performed on three different systems. Two smaller systems for which optimal solutions are known have been used to tune the main parameters of the simulated annealing process. The simulated annealing method has then been applied to a larger example system for which no optimal solutions are known: as a result an entire family of interesting solutions have been obtained with costs about 7% less than the best solutions known for that particular example system.

Optimal Capacitor Placement in Radial Distribution Networks

The capacitor placement (replacement) problem for radial distribution networks determines capacitor types, sizes, locations, and control schemes. Optimal capacitor placement is a hard combinatorial problem that can be formulated as a mixed integer nonlinear program. Since this is a nonpolynomial time (NP) complete problem, the solution approach uses a combinatorial search algorithm. The paper proposes a hybrid method drawn upon the Tabu search approach, extended with features taken from other combinatorial approaches such as genetic algorithms and simulated annealing, and from practical heuristic approaches. The proposed method has been tested in a range of networks available in the literature with superior results regarding both quality and cost of solutions.

Tabu search algorithm for network synthesis

Large scale combinatorial problems such as the network expansion problem present an amazingly high number of alternative configurations with practically the same investment, but with substantially different structures (configurations obtained with different sets of circuit/transformer additions). The proposed parallel tabu search algorithm has shown to be effective in exploring this type of optimization landscape. The algorithm is a third generation tabu search procedure with several advanced features. This is the most comprehensive combinatorial optimization technique available for treating difficult problems such as the transmission expansion planning. The method includes features of a variety of other approaches such as heuristic search, simulated annealing and genetic algorithms. In all test cases studied there are new generation, load sites which can be connected to an existing main network: such connections may require more than one line, transformer addition, which makes the problem harder in the sense that more combinations have to be considered.

Multistage and coordinated planning of the expansion of transmission systems

In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning. This is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity. The GA presented has a set of specialized genetic operators and an efficient form of generation of the initial population that finds high quality suboptimal topologies for large size and high complexity systems. In these systems, multistage and coordinated planning present a lower investment than static planning. Tests results are shown in one medium complexity system and one large size high complexity system.

A Bilevel Approach to Transmission Expansion Planning Within a Market Environment

We present a bilevel model for transmission expansion planning within a market environment, where producers and consumers trade freely electric energy through a pool. The target of the transmission planner, modeled through the upper-level problem, is to minimize network investment cost while facilitating energy trading. This upper-level problem is constrained by a collection of lower-level market clearing problems representing pool trading, and whose individual objective functions correspond to social welfare. Using the duality theory the proposed bilevel model is recast as a mixed-integer linear programming problem, which is solvable using branch-and-cut solvers. Detailed results from an illustrative example and a case study are presented and discussed. Finally, some relevant conclusions are drawn.

Parallel simulated annealing applied to long term transmission network expansion planning

The simulated annealing optimization technique has been successfully applied to a number of electrical engineering problems, including transmission system expansion planning. The method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Moreover, it has the ability to provide solutions arbitrarily close to an optimum (i.e. it is asymptotically convergent) as the cooling process slows down. The drawback of the approach is the computational burden: finding optimal solutions may be extremely expensive in some cases. This paper presents a parallel simulated annealing (PSA) algorithm for solving the long-term transmission network expansion planning problem. A strategy that does not affect the basic convergence properties of the sequential simulated annealing algorithm have been implemented and tested. The paper investigates the conditions under which the parallel algorithm is most efficient. The parallel implementations have been tested on three example networks: a small 6-bus network; and two complex real-life networks. Excellent results are reported in the test section of the paper: in addition to reductions in computing times, the PSA algorithm proposed in the paper has shown significant improvements in solution quality for the largest of the test networks.

A zero-one implicit enumeration method for optimizing investments in transmission expansion planning

This paper presents a zero-one implicit enumeration method applied to an integer programming subproblem which has to be solved as part of a more general process of obtaining an optimal solution for a transmission expansion planning problem by hierarchical Benders decomposition. The proposed algorithm has been successfully implemented and tested in a real-life system. The reasons why the implicit enumeration approach is particularly suited for the static expansion planning problem are fully discussed in the paper. >

Comparative studies on nonconvex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of nonconvex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

Transmission Network Expansion Planning Considering Uncertainty in Demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand