Power system operation and planning

The CIGRÉ C6 Study Committee (Distribution Systems and Dispersed Generation) considers the different aspects of integration of distributed generation. In this context, the C6.19 Working Group (WG) is specifically focused on “Planning and optimization methods for active distribution systems”. This report assesses the various requirements to facilitate the transition towards active distribution systems (ADSs). Specifically, the report starts from a survey of requirements of planning methodologies and identifies a new framework and methodologies for short, medium and long term models for active distribution planning. Active distribution networks (ADNs) have systems in place to control a combination of distributed energy resources (DERs), defined as generators, loads and storage. With these systems in place, the AND becomes an Active Distribution System (ADS). Distribution system operators (DSOs) have the possibility of managing the electricity flows using a flexible network topology. DERs take some degree of responsibility for system support, which depends on suitable regulatory environments and connection agreements. Distribution operation and planning stages can no longer be considered as separate tasks in the distribution business since the exploitation of existing assets with Advanced Automation and Control may be a valuable alternative to network expansion or reinforcement. Information and communication technology (ICT) cannot be considered as a simple add-on of the power system and simultaneous analysis (co-simulation) of both power system and ICT system behavior is required for planning and reliability studies, as well as risk analysis. Smart meters and automatic meter reading (AMR) offer a huge amount of data for load profiling and modelling. Data analytics and big data are key drivers for planning, since essential information has to be captured from on line measurement and raw data be used directly in real world planning applications. The ADS planning asks for daily customers profile with a probabilistic representation to take account of uncertainties that characterize their behaviour. Finally, interesting methodologies and models have been extracted from relevant publications. Particular attention has been paid to the planning tools, which are ready to be transferred into scaled industrial applications by means of strong interaction with R&D distribution company centres and Academia.

This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration
(DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant
of DG units in order to take the transient stability of DGs into account is one of the major issues in power
systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system
transient stability may be neglected. However, when the penetration of DG increases, the transient stability
of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem
has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability
index and decrease losses and operation cost in a distribution test system with multiple micro-turbines.
The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting
close to the practical condition and considering the detailed dynamic models of the generators and other
electric devices in power system, simulation and programming of this approach are done by the
DIgSILENT Power Factory software.

Micro-grids (MGs) are introduced as a solution for distributed energy resource (DER) units and energy storage systems (ESSs) to participate in providing the required electricity demand of controllable and non-controllable loads. In this paper, the authors study the short-term scheduling of grid-connected industrial heat and power MG which contains a fuel cell (FC) unit, combined heat and power (CHP) generation units, power-only unit, boiler, battery storage system, and heat buffer tank. The paper is aimed to solve the multi-objective MG dispatch problem containing cost and emission minimization with the considerations of demand response program and uncertainties. A probabilistic framework based on a scenario method, which is considered for load demand and price signals, is employed to overcome the uncertainties in the optimal energy management of the MG. In order to reduce operational cost, time-of-use rates of demand response programs have been modeled, and the effects of such programs on the load profile have been discussed. To solve the multi-objective optimization problem, the e-constraint method is used and a fuzzy satisfying approach has been employed to select the best compromise solution. Three cases are studied in this research to confirm the performance of the proposed method: islanded mode, grid-connected mode, and the impact of time of the use-demand response program on MG scheduling.

Optimal generation scheduling of hydro-based power units has a significant place in electric power systems, which considerably has been dealt with as a subject of investigations for several years. Hydrothermal system is introduced as an important hydro-based power generation system. The objective of short-term hydrothermal scheduling (STHS) problem is obtaining the power generation schedule of the available hydro and thermal power units, which aims to minimize total fuel cost of thermal plants during a determined time period. Many conventional optimization procedures are first introduced for solving STHS problem. Recently, heuristic and meta-heuristic optimization methods, which are defined as an experience-based procedure, are implemented for obtaining optimal solution of generation planning of hydrothermal systems. This paper provides a comprehensive review on the application of heuristic methods to obtain optimal generation scheduling of hydrothermal systems, which compares the implemented procedures from different points of view. Optimal solutions obtained by employment of multiple heuristic and meta-heuristic optimization methods for different test instances are demonstrated and the introduced methods are compared in terms of convergence speed, attained optimal solutions, and constraints. Future research trends are discussed, which can be introduced as the subject of studies in the area of STHS problem.

The distribution of electrical energy to end-users in Nigerian communities is faced with diverse spatial problems leading to low voltage, overload on equipment, difficulties in fault tracing and delay in fault clearing. The traditional management system is not only manual but also has flaws such as difficulties in searching and updating previous records as well as no real-time information on their distribution assets. In this study, a geospatial technique was employed for effective management of the electricity distribution system in Malete, Kwara State, Nigeria. Spatial information of distribution asset was collected from the field and used in the structural modelling of the distribution network in the ArcMap software. Analysis of the model and the available information showed that 80% of the transformers were not properly located at the centroid of the load. The span of the network fairly conformed to standard as only 45.73% were within 45-50m. The result also showed that 40% of the transformers were overloaded with an unbalanced load, hence the need for restructuring of the network

Power loss reduction can be considered as one of the main purposes for distribution system operators. Reconfiguration
is an operation process used for this optimisation by means of changing the status of switches in a distribution network. Recently,
all system operators tried their best in order to obtain well-balanced distribution systems to decrease the operation cost, improve
reliability and reduce power loss. This study presents an efficient method for solving the multi-objective reconfiguration of radial
distribution systems with regard to distributed generators. The conventional distribution feeder reconfiguration (DFR) problem
cannot meet the reliability requirements, because it only considers loss and voltage deviation as objective functions. The
proposed approach considers reliability, operation cost and loss simultaneously. By adding the reliability objective to the DFR
problem, this problem becomes more complicated than before and it needs to be solved with an accurate algorithm. Therefore
this study utilises an Enhanced Gravitational Search Algorithm called EGSA which profits from a special mutation strategy in
order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima.
The proposed approach has been applied to two distribution test systems including IEEE 33 and 70-node test systems.

—This paper presents a computer-assisted method to teach and understand transmission pricing techniques. The education method is facilitated by specially designed user-friendly transmission pricing software (TPS). The proposed approach helps students fully understand transmission pricing through the fulfillment of the following educational objectives: 1) use of TPS to solve the transmission pricing problem of a small power system as well as a real-world power system; 2) complete understanding of the mathematics involved and development of software code to reproduce part of the results of TPS; 3) analysis of the results, identification and justification of the differences among three transmission tracing techniques and eight transmission cost allocation methods; and 4) extension of TPS methodologies. These methods have been proven effective in the education of students studying power system economics at the National Technical University of Athens (NTUA), Greece. A full-fledged, robust, comprehensive study of the educational improvements achieved through the use of TPS is provided.

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important easures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

The short-term hydrothermal scheduling (STHS) problem is providing a daily planning of hydro and thermal generations, aiming to minimize the total fuel cost of thermal plants. The minimization of total operation cost of hydrothermal power system is considered as a complex nonlinear hard optimization problem with a series of several equality and inequality constraints. This paper proposes real-coded genetic algorithm with an improved Mühlenbein mutation (RCGA-IMM) for the solution of STHS optimization problem, considering the minimization of operation cost which satisfies hydraulic and electrical constraints. The proposed optimization procedure is employed on two test systems in which different constraints have been taken into account including valve point loading effect of thermal units and transmission losses. The provided optimal solutions have been compared with recent studies in this area, which manifest superiority of the proposed method. It is found that the proposed RCGA-IMM has the capability of obtaining better solutions with respect to other optimization methods which are implemented on STHS problem.