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In this paper a sizing tool is proposed for Photovoltaic (PV) and Battery Energy Storage Systems (BESS) in installations with Demand Side Management capability. Three objectives functions are defined, describing the Self-Sufficiency Rate, the Net-Present Value of the investment in PV and BESS, and their combination in a joint expression, respectively. Two analysis methods are developed within the tool to find the optimal solution. The first one applies an exhaustive search by examining all possible combinations of PV and BESS capacity, under pre-defined load shifting. The drawback of this method is the high computational burden due to unnecessary access in the solution space. The second method is implemented with a Particle Swarm optimization (PSO) variant, namely the Unified-PSO. Under this approach, the analysis algorithm is guided in the solution space towards efficient solutions under lower computational time. The comparative assessment of the two methods verifies that via the PSO algorithm the optimal solutions are found under almost half execution time.

Being an engineering field, power systems provide an extensive subject for optimization to be applied upon. Modern power systems have evolved in an increasingly highly complex system. The liberalization of the energy market and the introduction of distributed generation and, in particular, distributed renewable energy resources (DRES) have raised both opportunities and challenges that need to be tackled. Thus, complex issues related to the operation and planning of the distribution systems have emerged. Such issues involve many variables and refer to nonlinear objectives; thus their optimization is significantly based on heuristic techniques, such as particle swarm optimization (PSO). In this chapter, the implementation of PSO when contemplating various problems in power systems is presented. In particular, the utilization of PSO is demonstrated in the optimal distributed generation placement problem (ODGP), also known as optimal siting and sizing of distributed generation problem, and in the network reconfiguration problem. Finally, PSO is implemented in an optimal schedule of electric vehicles (EVs) charging, providing an apt example of the variety of problems for which PSO can be utilized and providing useful aid to important decisions, in the field of power systems.

The Optimal Distributed Generation Placement problem (ODGP) towards energy loss minimization depends basically on the network's layout and its load composition. Under load variations, different load compositions result, for each one of them, is highly possible to come up with a different optimal solution regarding the optimal siting and sizing of DG units. This paper examines the impact of these variations in order to verify how optimal solution should adapt to any load composition. A Local Particle Swarm Optimization Variant algorithm is proposed as the solution algorithm and numerous load composition snapshots for the IEEE-33 bus system are examined. Moreover, a methodology is proposed in order to highlight the critical nodes that prove to have an essential role to the solution. Finally, the possibility for the determination of a fixed solution with fixed installation nodes and constant power output that could yield near optimal energy loss reduction is examined.

Integration of distributed generation (DG) in existing distribution networks has been studied thoroughly during the past years as a measure of reducing grid's power losses. However, the optimal DG placement, known as ODGP, toward loss minimisation, has not been studied in depth by considering the possible impact of the reverse power flow (RPF) caused by extended penetration of distributed energy resources. This study uses a constriction factor embedded local particle swarm optimisation algorithm along with the appropriate particle formulation that solves the ODGP problem by taking into account the impact of possible RPF. In this study, the idea of RPF modelling is introduced by providing extended versions of IEEE test systems. Modified versions of the IEEE 30-bus and IEEE 33-bus test systems are modelled and results are presented in order to highlight the impact of RPF on the ODGP problem solution. The mathematical formulation is given, results and analysis for both extended systems are presented, while the importance of RPF for different conditions is assessed.

The scope of the present study is the assessment of a Particle Swarm Optimization (PSO) method for optimal input selection for Short-Term Load Forecasting (STLF) tasks. The test case involves the STLF of a high to medium voltage transformer, where a Feed-Forward Neural Network (FFNN) is trained and applied. An effective training process is crucial to the forecasting model's success. Low number of inputs can lead to low potential for generalization. On the contrary, a high number may lead to large training duration. Thus, a trade-off between the generalization and duration should be accomplished. Using the PSO based method, the most correlated inputs are selected, a fact that leads to low prediction errors.

Natural gas is considered as a crucial power source both for electrical and thermal loads. Natural gas markets are continually expanding, a fact that raises the need of formulating new energy services to the end-users. The aim of the present paper is to develop a two-stage clustering process that leads to the formation of natural gas demand profiles per distribution point. Natural gas demand profiles can be further utilized in a variety of applications such as forecasting, tariff designs and others. The test case refers to the distribution points of the national natural gas system of Greece. The results show that hierarchical algorithms are the suitable clustering solution for the dataset under study.

In this paper a novel approach regarding the optimal penetration of Distributed Generation (DG) in Distribution Networks (DNs) towards loss minimization is proposed. More specific, a Local Particle Swarm Optimization (PSO) variant algorithm is developed in order to define the optimal active and reactive power generation and/or consumption requirements for the optimal number and location of nodes that yield loss minimization. Thus, the proposed approach provides the optimal number, siting and sizing of DGs altogether. In addition, based on the optimal power requirements of the resulted nodes, a combination of potential DG types to be installed is recommended. The proposed objective function in this paper is also innovative since it embeds the constraint of reverse power flow to the slack bus by the formation of a new penalty term. The proposed methodology is applied to 30 and 33 bus systems. The results indicate the optimal number, locations, and capacity of DG units, which were calculated simultaneously. Finally, the impact of the predefined amount of permissible reverse power flow to the optimal solution is also examined through two scenarios: the first considers zero reverse power flow and the second unlimited reverse power flow.

This chapter introduces the Optimal Distributed Generation Placement problem towards power and energy loss minimization. Several solving methods are applied in order for the most suitable to emerge. Apart from technical and DG constraints, recent raised issues due to high Distributed Generation penetration like the reverse power flow effect is considered as well. The load and generation variability and their impact in integrating Renewable Energy Sources are examined, aided by the use of Capacity Factors implementation. In addition, the impact of Optimal Distributed Generation Placement problem in conjunction with Network Reconfiguration and Optimal Energy Storage Systems Placement is introduced aiming to examine how joined management schemes could be efficiently combined in order to maximize the potential loss and energy reduction.

In this paper, a comparative analysis and evaluation of several heuristic techniques, when applied to the Optimal Distributed Generation Placement (ODGP) problem, is presented. Loss minimization is considered as the objective, while the network's technical characteristics as the constraints. Three versions of Particle Swarm Optimization (PSO), Global, Local and Unified (GPSO, LPSO and UPSO, respectively), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS) and Harmony Search (HS) are compared. The implemented analysis demonstrates that all Heuristic Techniques examined can solve the ODGP problem efficiently, although UPSO emerge as the most promising, in terms of solution and convergence performance, whereas regarding execution time, HS is the most prominent. The study is evaluated upon typical 33 and 30 bus systems.

In this paper loss reduction in Distribution Networks is performed under proper consideration regarding Distributed Generation (DG) siting and sizing and network reconfiguration. The optimal solution for each of the aforementioned techniques, when applied individually, is highly dependent on the network layout and on its load composition. Thus, the question that arises when both techniques are utilized, concerns their application order towards the most efficient solution. The analysis in this work examines how the sequence regarding the implementation of these two techniques affects the optimal solution in terms of DG capacity penetration and number of switching operations. Various scenarios with different available DG units are examined regarding their optimal siting and sizing before and after the implementation of the network reconfiguration technique. Finally, an alternative scenario with the simultaneous application of both techniques is also examined. In this latter case the complexity of the problem is highly increased and the solution efficiency is also affected. A special PSO variant is utilized as the solution algorithm, namely Unified-PSO, accordingly modified for each scenario and the IEEE 33 bus system is utilized as the reference distribution network. The analysis concludes about the optimal application order of these two techniques for practical implementation.

In this paper the Optimal Distributed Generation Problem (ODGP) towards energy minimization is solved for a large number of scenarios regarding power loss minimization. Load variations are taken into account by the formulation of different snapshots concerning the network's operational status. These snapshots refer to various load compositions and for each one the ODGP problem is applied. Load variations are formed stochastically under a uniform distribution while the initial loading conditions are considered as the mean load profile of the network. The solution algorithm relies on a Local PSO Variant and the results indicate that for not extreme load variations some specific nodes tend to participate in the majority of the different solutions. Thus, the analysis proposes a fixed solution that could yield the highest energy reduction despite the fact that it is not the optimal for each individual operating state with different load composition.

In this paper, a new effort regarding the Optimal Distributed Generation Placement (ODGP) problem is presented. Loss minimization is considered as the objective while considering the network's technical characteristics as constraints, i.e. node voltage and line thermal limits. The proposed method, called Unified Particle Swarm Optimization technique (UPSO), combines the advantages while at the same time extinguishes the disadvantages of the two basic PSO variants, the Global and Local PSO. The implemented analysis demonstrates that an enhanced performance is achieved, both in terms of a better optimal solution as well as faster convergence. The method is evaluated upon IEEE-16 and IEEE-33 bus systems and compared with other techniques.

Increasing penetration of Distributed Generation (DG), when not planned appropriately by the Distribution Network Operator may lead to opposite results that are displaced as externalities on adjacent grids. Reverse Power Flow (RPF) by means of an upstream power flow leaving the grid under study and heading to the neighbouring ones could be such an issue and need to be considered and modelled accordingly. This paper introduces two implementations of constriction factor Embedded-Local PSO Variant algorithms along with the appropriate particle formulation that focus on the Optimal Distributed Generation Placement (ODGP) problem solving, by taking into account the impact of possible RPF. Both RPF methods are applied to either radial or meshed IEEE test systems and a detailed comparison is presented. Focus is given on how the integration of RPF would affect the final solution to the ODGP problem, but the same RPF modelling strategies could be applied under different target functions as well.

The increased penetration of Renewable Energy Resources (RESs) and Distribution Generation (DG) units in Distribution Networks (DGs) is expected to cause operational states subject to downstream power flow violations. The branches with upstream power flow could undergo high loading levels and thus, their carrying current could increase towards their ampacity level. Moreover, some nodes could experience over voltages; hence the Distribution System Operator (DSO) would probably proceed in forcing the dispersed generators to reduce their power injection into the grid or even in cutting of specific producers for as long it is needed. In this paper, two factors are proposed in order to describe and quantify the congestion level for DNs. Moreover, the problem is proposed to be solved via the technique of network reconfigurations in order to mitigate the congestion level. Numerous tests show that limited switching operations under simple heuristic rules could efficiently deal with the congestion problem.

This paper investigates the influence of Distributed Generation (DG) units to the optimal reconfiguration of Distribution Networks (DNs) for loss reduction. The concept relies on load variations caused by the local generation, which results in different load composition in the network compared to the no DG case. A comprehensive investigation of all possible combinations regarding potential DG connection nodes has been implemented in a 16 and a 33 bus system. Results indicate that a limited number of branches, usually adjacent to the DGs, participate in the optimal solution, regardless of the load composition. Moreover, a quantified correlation of the DN nodes that mostly affect the problem solution is presented, based on the participation frequency of the corresponding loads.

In this paper a methodology regarding the siting and sizing of Photovoltaic Systems (PVS) under the consideration of several parameters is presented. In the proposed algorithm equal distribution of the available PVS is initially considered and at the next level appropriate coefficients are formalized in order to quantify each respective parameter. The initial equal distributed capacity of PVS is being resized due to the impact of various critical parameters. Moreover, the participation of each parameter to the resizing procedure is being defined by respective weight factors. In the paper, the Interconnected Greek Transmission System (IGTS) is examined as the test case and useful conclusions are depicted regarding the algorithm's results. The proposed algorithm could constitute a useful guideline for efficient allocation of PVS in regard to specific predefined requirements and in turn it could define the most appropriate solution among a number of alternatives.