Scheduling of head-dependent cascaded reservoirs considering discharge ramping constraints and start/stop of units

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning head-dependent reservoirs under competitive environment. We propose a novel method, based on mixed-integer nonlinear programming (MINLP), for optimising power generation efficiency. This method considers hydroelectric power generation as a nonlinear function of water discharge and of the head. The main contribution of this paper is that discharge ramping constraints and start/stop of units are also considered, in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve two case studies based on Portuguese cascaded hydro systems, providing a higher profit at an acceptable computation time in comparison with classical optimisation methods based on mixed-integer linear programming (MILP).

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Phase diagrams of binary mixtures of patchy colloids with distinct numbers and types of patches: Theempty fluid regime

We investigate the effect of distinct bonding energies on the onset of criticality of low functionality fluid mixtures. We focus on mixtures ofparticles with two and three patches as this includes the mixture where "empty" fluids were originally reported. In addition to the number of patches, thespecies differ in the type of patches or bonding sites. For simplicity, we consider that the patches on each species are identical: one species has threepatches of type A and the other has two patches of type B. We have found a rich phase behavior with closed miscibility gaps, liquid-liquid demixing, and negative azeotropes. Liquid-liquid demixing was found to pre-empt the "empty" fluid regime, of these mixtures, when the AB bonds are weaker than the AA or BB bonds. By contrast, mixtures in this class exhibit "empty" fluid behavior when the AB bonds are stronger than at least one of the other two. Mixtureswith bonding energies epsilon(BB) = epsilon(AB) and epsilon(AA) < epsilon(BB), were found to exhibit an unusual negative azeotrope. (C) 2011 American Institute of Physics. [doi:10.1063/1.3561396]

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Addressing the facilities layout design problem through constraint logic programming

One of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm.

An efficient method for optimal location and sizing of fixed and switched shunt capacitors in large distribution systems

This paper proposes a computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in large distribution systems. The problem is formulated as the maximization of the savings produced by the reduction in energy losses and the avoided costs due to investment deferral in the expansion of the network. The proposed method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteristics, i.e. fixed or switched. After an appropriate linearization, the optimization problem was formulated as a large-scale mixed-integer linear problem, suitable for being solved by means of a widespread commercial package. Results of the proposed optimizing method are compared with another recent methodology reported in the literature using two test cases: a 15-bus and a 33-bus distribution network. For the both cases tested, the proposed methodology delivers better solutions indicated by higher loss savings, which are achieved with lower amounts of capacitive compensation. The proposed method has also been applied for compensating to an actual large distribution network served by AES-Venezuela in the metropolitan area of Caracas. A convergence time of about 4 seconds after 22298 iterations demonstrates the ability of the proposed methodology for efficiently handling large-scale compensation problems.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.