Manuel A. Matos

Optimization of Pumped Storage Capacity in an Isolated Power System With Large Renewable Penetration

This work describes an economic analysis of the inclusion of pumped storage in a small island system that has abundant renewable energy available but that at times cannot accept all of this power because of limits imposed by security criteria. The question of whether or how much pumped storage to include is addressed by formulating a linear programming optimization problem. The stochastic nature of load and renewable production is addressed using scenarios developed through fuzzy clustering. Both the unit capacity in MW and the reservoir storage capacity in MWh are optimized, and optimal operating strategies for the scenarios are produced. Results showed that including pumped storage can be an effective means of allowing larger penetration of intermittent renewable energy sources, improving both the dynamic security and the economic operation of a test system. Including the dynamic security criteria in the economic question of dimensioning the pumped storage unit proved to make a significant difference in the optimal pumped storage capacity.

Setting the Operating Reserve Using Probabilistic Wind Power Forecasts

In power systems with a large integration of wind power, setting the adequate operating reserve levels is one of the main concerns of system operators (SO). The integration of large shares of wind generation in power systems led to the development of new forecasting methodologies, including probabilistic forecasting tools, but management tools able to use those forecasts to help making operational decisions are still needed. In this paper, a risk evaluation perspective is used, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision making. The new reserve management tool (RMT) described in the paper is intended to support the SO in defining the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. An illustrative example based on the Portuguese power system demonstrates the usefulness and efficiency of the tool.

Optimized Bidding of a EV Aggregation Agent in the Electricity Market

An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naïve forecast for all variables of the problem.

Loss allocation in distribution networks with embedded generation

This paper addresses the allocation of electrical losses in distribution networks with embedded generation, in a liberalized environment. The nonlinear nature of the issue, the loss changes due to voltage variation and, specially, the contribution of embedded generation to loss variation are considered. The proposed method is based on tracing the real and imaginary parts of the currents and has two steps. First, the losses in the distribution network, in the absence of embedded generation, are allocated to the consumers (or their providers). Second, the variations in the losses that result from the influence of embedded generation are allocated to the generators. These variations are a measure of the avoided or added costs related to losses. In the allocation process, made in a branch basis, both real and reactive powers are considered. The methodology presented in this paper can be used to evaluate embedded generation incentives or to design tariffs for the use of the distribution network.

Distribution Systems Reconfiguration Based on OPF Using Benders Decomposition

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, load balancing among feeders, and is subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages, and radial optimal operation of networks. A specific approach of the generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages: the first one is the master problem and is formulated as a mixed integer nonlinear programming problem. This stage determines the radial topology of the distribution network. The second stage is the slave problem and is formulated as a nonlinear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in the general algebraic modeling system. The effectiveness of the proposal is demonstrated through three examples extracted from the literature.

Multiobjective Optimization Applied to Maintenance Policy for Electrical Networks

A major goal for managers of electric power networks is maximum asset performance. Minimal life cycle cost and maintenance optimization becomes crucial in reaching this goal, while meeting demands from customers and regulators. This necessitates the determination of the optimal balance between preventive and corrective maintenance in order to obtain the lowest total cost. The approach of this paper is to study the problem of balance between preventive and corrective maintenance as a multiobjective optimization problem, with customer interruptions on one hand and the maintenance budget of the network operator on the other. The problem is solved with meta-heuristics developed for the specific problem, in conjunction with an evolutionary particle swarm optimization algorithm. The maintenance optimization is applied in a case study to an urban distribution system in Stockholm, Sweden. Despite a general decreased level of maintenance (lower total maintenance cost), better network performance can be offered to the customers. This is achieved by focusing the preventive maintenance on components with a high potential for improvements. Besides this, this paper displays the value of introducing more maintenance alternatives for every component and choosing the right level of maintenance for the components with respect to network performance.

Economic Analysis of Microgrids Including Reliability Aspects

Recently, the new concept of microgrid (muG) has been emerging on distribution networks as a way to ease the integration of micro generation in LV networks and increase reliability. A muG is an association of a low voltage distribution network, small modular generation systems (micro-generators), loads and storage devices having some local coordinated functions. This entity can operate in two different modes: interconnected or emergency. In the first mode, the microgrid is connected with the distribution network, importing or exporting electricity and/or ancillary services. When in emergency mode, the microgrid operates isolated from the distribution network and uses local resources, changing from power control to frequency control and, if necessary, shedding load. A micro grid will only be established if its promoters achieve sufficient advantages that justify the incurred costs, namely the investment, operation and maintenance costs. The main purpose of this paper is to identify all the relevant costs and benefits and build a decision model for the situation, taking into account the regulatory framework, which is essential for the definition of some of the benefits. The paper also shows how to include in the evaluation the risk associated to the uncertainties in data and parameters. An illustrative example is included that shows a possible situation of equilibrium between global costs and benefits

Decision under risk as a multicriteria problem

Abstract Most of the approaches to decision problems under uncertainty are based on decision paradigms, generally associated to an optimization process that leads to a final solution. For the Decision Maker, the basic decision is thus what paradigm to choose, the rest of the procedure being mainly technical. In this paper, a different approach is advocated for this kind of problems. The main idea is to leave prescriptive models in favor of a more flexible approach, where risk related criteria are explicitly considered, conducting to an “equivalent” multicriteria (deterministic) model where decision-aid procedures can be used, with a greater involvement of the Decision Maker. The paper discusses first the uncertainty model and then reviews existing paradigms for the single criterion problem under uncertainty. Proposed risk and opportunity attributes come mainly from the analysis of those methodologies and from risk perception studies reports. Some hints about multicriteria aid methods and an illustrative example complete the paper.

Optimization Models for EV Aggregator Participation in a Manual Reserve Market

The charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.

Reserve Setting and Steady-State Security Assessment Using Wind Power Uncertainty Forecast: A Case Study

This paper reports results and an evaluation methodology from two new decision-aid tools that were demonstrated at a Transmission System Operator (REN, Portugal) during several months in the framework of the E.U. project Anemos.plus. The first tool is a probabilistic method intended to support the definition of the operating reserve requirements. The second is a fuzzy power flow tool that identifies possible congestion situations and voltage violations in the transmission network. Both tools use as input probabilistic wind power predictions.