Davor Škrlec

Supervisory Control of an Adaptive-Droop Regulated DC Microgrid With Battery Management Capability

DC power systems are gaining an increasing interest in renewable energy applications because of the good matching with dc output type sources such as photovoltaic (PV) systems and secondary batteries. In this paper, several distributed generators (DGs) have been merged together with a pair of batteries and loads to form an autonomous dc microgrid (MG). To overcome the control challenge associated with coordination of multiple batteries within one stand-alone MG, a double-layer hierarchical control strategy was proposed. 1) The unit-level primary control layer was established by an adaptive voltage-droop method aimed to regulate the common bus voltage and to sustain the states of charge (SOCs) of batteries close to each other during moderate replenishment. The control of every unit was expanded with unit-specific algorithm, i.e., finish-of-charging for batteries and maximum power-point tracking (MPPT) for renewable energy sources, with which a smooth online overlap was designed and 2) the supervisory control layer was designed to use the low-bandwidth communication interface between the central controller and sources in order to collect data needed for adaptive calculation of virtual resistances (VRs) as well as transit criteria for changing unit-level operating modes. A small-signal stability for the whole range of VRs. The performance of developed control was assessed through experimental results.

Advanced LVDC Electrical Power Architectures and Microgrids: A step toward a new generation of power distribution networks.

Current trends indicate that worldwide electricity distribution networks are experiencing a transformation toward direct current (dc) at both the generation and consumption level. This tendency is powered by the outburst of various electronic loads and, at the same time, the struggle to meet the lofty goals for the sharing of renewable energy sources (RESs) in satisfying total demand. RESs operate either natively at dc or have a dc link in the heart of their power electronic interface, whereas the end-point connection of electronic loads, batteries, and fuel cells is exclusively dc. Therefore, merging these devices into dedicated dc distribution architectures through corresponding dc?dc converters is an attractive option not only in terms of enhancing efficiency because of reduction of conversion steps but also for realizing power quality independence from the utility mains. These kinds of systems generally provide improved reliability in comparison to their alternating current (ac) counterparts since the number of active elements in dc?dc power electronic devices is smaller than in dc-ac converters. Control design in dc systems is also significantly simpler since there are no reactive and harmonic power flows or problems with synchronization.

Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility

This paper describes a robust optimization approach to minimize the total cost of supplying a remote telecommunication station exclusively by renewable energy sources (RES). Due to the intermittent nature of RES, such as photovoltaic (PV) panels and small wind turbines, they are normally supported by a central energy storage system (ESS), consisting of a battery and a fuel cell. The optimization is carried out as a robust mixed-integer linear program (RMILP), and results in different optimal solutions, depending on budgets of uncertainty, each of which yields different RES and storage capacities. These solutions are then tested against a set of possible outcomes, thus simulating the future operation of the system. Since battery cycling is inevitable in this application, an algorithm that counts the number of cycles and associated depths of discharges (DoD) is applied to the optimization results. The annual capacity reduction that results from these cycles is calculated for two types of battery technologies, i.e., valve-regulated lead-acid (VRLA) and lithium-ion (Li-ion), and treated as an additional cost. Finally, all associated costs are added up and the ideal configuration is proposed.

Genetic Algorithm Approach for Multiple Depot Capacitated Vehicle Routing Problem Solving With Heuristic Improvements

Abstract The paper proposes a Genetic Algorithm (GA) in conjunction with handy heuristic techniques to solve the non-fixed destination Multiple Depot Capacitated Vehicle Routing Problem (MDCVRP). New heuristic techniques are added in order to prevent converging to local optima and to speed up the convergence of the algorithm through a reduction of the search space domain. The proposed GA approach has been tested on several instances of practical longterm link distribution network design problems which can be easily correlated with the non-fixed destination MDCVRP. Test results reveal that the features of easy implementation, fast convergence, and a near optimal solution in solving the MDCVRP can be achieved with the proposed GA approach.

Darwin meets computers: new approach to multiple depot capacitated vehicle routing problem

We present a study of using genetic algorithms (GAs) to solve non-fixed destination multiple-depot capacitated vehicle routing problem. The genetic algorithm was developed on the basis of experiences in solving the travelling salesman problem and the single depot capacitated vehicle routing problem. Heuristic improvements in population initialization and crossover operators are made to prevent converging to local optima and to reduce the search space domain. To deal effectively with the constraints of the problem, and to prune the search space of GA in advance, the difficult capacity and supply reliability constraints are embedded in the decimal strings that are coded to represent the vehicle routes between depots. Computational results carried out on several instances indicate that the total distance travelled can be reduced significantly when such method is used.

A heuristic modification of genetic algorithm used for solving the single depot capacited vehicle routing problem

The paper proposes a genetic algorithm (GA) in conjunction with handy heuristic techniques to solve the single depot capacited vehicle routing problem (CVRP). The genetic algorithm is developed on the basis of experiences in solving the travelling salesman problem (TSP). Few heuristic improvements are added in order to prevent converging to local optima and to reduce the search space domain. To deal effectively with constraints of the problem and prune the search space of the GA in advance, the constraints are embedded in decimal coding of the problem. The proposed GA approach has been tested on several CVRP with different number of consumer nodes and different control parameters. The results of 144 node problem used for sensitivity analysis, reveal that the features of easy implementation, fast convergence, and near optimal solution in solving the CVRP can be achieved by the proposed GA approach.

Genetic algorithm and GIS enhanced long term planning of large link structured distribution systems

In order to enhance the serviceability in the distribution system genetic algorithm and GIS based method is proposed in this article for planning the link distribution networks. All practical issues such as cost parameters (investments, line losses, maintenance), and technical constraints (voltage drop, thermal limit, reliability) as well as physical routing constraints (obstacles, high cost passages, existing line sections) are taken into consideration. Fuzzy set concept and scenario representation (tree of futures) to model uncertainties, as well as decision making guided by a paradigm of multi-criteria risk analysis are discussed. The merits of the approach are discussed by analyzing its application to a study case based on a real case in a Croatian utility.

The genetic algorithm method for multiple depot capacitated vehicle routing problem solving

Many organizations face the problem of delivering goods from a certain number of warehouses to a number of retail sites using a fleet of vehicles. The multiple depot capacitated vehicle routing problem is mathematical model that closely approximates the problem faced by many of these organizations. Because the problem is NP-hard, requiring excessive time to be solved exactly, we develop a heuristic based on a genetic algorithm that finds high quality solutions in a reasonable amount of computer time. Basic GA procedures adapted to a given problem are presented and six versions of crossover operators are compared. The test results reveal that the method is able to produce results of a kind that are not easily obtained, namely in terms of the amount of information about the solutions and the solution space.

An efficient implementation of genetic algorithms for constrained vehicle routing problem

We propose a genetic algorithm based heuristic for solving the problem of open loop distribution network planning. The goal of power distribution system planning is to satisfy the growth and changing system load demand during the planning period and within operational constraints, with minimal costs. Although the algorithm was developed for specific real world problems, the method is quite general and can be encountered in many planning contexts that can be correlated with the well known Capacitated Vehicle Routing problem (CVRP). For the CVRP problem, the influences of the respective control parameters were examined. Also the issues regarding the usage of different selection parameters are examined, in order to observe their impact on the optimization procedure. The results of experiments testing the solution procedures are reported.

Artificial immune systems in solving routing problems

Successful planning of electrical distribution networks is a complex problem. Besides the well-known radial network layout, in Europe, particularly in Croatia, the distribution networks are also loop or link structured. In order to be solved, network optimization problem is translated into some known combinatorial problems. When networks are loop-structured, the traveling salesman problem (TSP) is commonly used. The combinatorial problems like TSP cannot be solved exactly and evolutionary algorithms have been successful in solving these problems. Although, they are shown as very efficient, progress is still expected concerning the stability and lesser dependency on input parameters. In addition, evolutionary techniques for solving combinatorial problems often do not pay attention to existing knowledge about the problem. An improvement of the classic genetic algorithm used for solving the TSP is shown here, inspired by artificial immune systems techniques. Special attention is also paid to object-oriented design of the application and achieved benefits.