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A way to improve energy management is to perform balancing both at the Peer-to-peer (P2P) level and then at the Virtual Microgrid-to-Virtual Microgrid (VMG2VMG) level, while considering the intermittency of available Renewable Energy Source (RES). This paper proposes an interdisciplinary analytics-based approach for the formation of VMGs addressing energy balancing. Our approach incorporates Computer Science methods to address an Energy sector problem, utilizing data preprocessing techniques and Machine Learning concepts. It features P2P balancing, where each peer is a prosumer perceived as an individual entity, and Virtual Microgrids (VMGs) as clusters of peers. We conducted several simulations utilizing clustering and binning algorithms for preprocessing energy data. Our approach offers options for generating VMGs of prosumers, prior to using a customized Exhaustive brute-force Balancing Algorithm (EBA). EBA performs balancing at the cluster-to-cluster level, perceived as VMG2VMG balancing. To that end, the study simulates on data from 94 prosumers, and reports outcomes, biases, and prospects for scaling up and expanding this work. Finally, this paper outlines potential ideal usages for the approach, either standalone or integrated with other toolkits and technologies.

In this paper, an unsupervised non-intrusive load monitoring approach is proposed in order to encounter the disaggregation problem for Non-Intrusive Load Monitoring (NILM) methodologies, using odd harmonic current amplitudes. The problem has been contemplated as a multi-class multi-label one and for the combinations examined the number of appliances operating simultaneously varies between from one to up to three appliances. K-means has been utilized to cluster the different combinations, using additionally the elbow technique, in order to obtain the most suitable number of clusters that should be created. The results indicate that the proposed technique performs efficient load identification even with few samples at the training stage especially under the consideration of the third and fifth odd harmonic currents.

Building energy consumption has been declared as an untapped energy source, accounting for 40% of energy consumption worldwide. In this paper, in order to decrease power consumption in an office building aiming at peak reduction a Demand Response solution is proposed integrated in a real building pilot site. Consumption profiling, day-ahead forecasting as long as various feedback techniques were implemented taking into consideration occupant thermal comfort aspects. To make the framework more accurate, various variables have been considered, such as weather forecast, along with environmental conditions (in- and outdoor temperature), occupancy and thermal comfort patterns, space occupancy and current operation of the system. A real-life demonstration of the proposed system was implemented in a commercial building in Thessaloniki Greece, integrating field equipment for real-time monitoring and control of several offices. Preliminary field test results of the working framework are included in this work.

In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

In the smart home context, one of the features that most users would desire is the knowledge of the operation of each electrical appliance without the need to install expensive smart plugs or measuring equipment at each outlet-appliance. The Non-Intrusive Load Monitoring (NILM) idea is an alternative method where only one meter is utilized. However, the accuracy and simplicity of NILM approaches are still under improvement and evolvement. In this paper we propose the development of load signatures by a limited number of harmonic current vectors. The rationale behind this is that simultaneously operating appliances may have opposite or displaced harmonic current vectors, which means that using only current amplitudes as load signatures will not result in reliable identification. Three alternative formations are examined aiming to highlight the contribution of the harmonic current phase angles to the robustness of the load signatures. The first formation considers only the current amplitude, the second takes into account the phase angles in the form of vector projection on the x-axis, and the third is based on harmonic current vectors. The respective disaggregation schemes are also proposed considering either amplitude summations or vector aggregations, in order to examine the correct appliance combination that matches the actual operating one. Measurements have been performed in a typical residential installation considering both stand-alone operation of appliances and scenarios with appliance combinations. The results indicate that disaggregation performance is significantly improved when relying on harmonic current vectors in respect to the case with only current amplitudes, which makes the methodology potentially suitable for the new smart meters that are expected to be widely installed.

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.

This paper examines how Low Voltage Distribution Networks (DNs) can optimize their voltage profile and minimize their energy losses through the planning ofcharging of Electric Vehicles (EVs).We consider variations concerning the charger and battery capacity of the EVs and we formulate two objective functions. A Particle Swarm Optimization (PSO) algorithm is utilized for the determination of the optimized charging planning of the EVs. The main idea is to examine both the Grid-to- Vehicle (G2V) and Vehicle-to-Grid (V2G) schemes for the EVs and evaluate how each of them contributes to the improvement of the operational characteristics of the DN. Thus, for the G2V scheme the EVs are considered only under charging mode and the algorithm provides their optimal charging planning for one day. Additionally, for the V2G scheme we consider the possibility for EV discharging as well. The simulations are carried out on a real DN with real loading data for the day of the year with the worst voltage profile. The results indicate that under proper charging planning the DN will have a smoother voltage profile with lower power losses. Moreover, the controlled V2G scheme can further improve this situation.

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.

This chapter introduces the Network Reconfiguration (NR) concept in Distribution Networks (DNs) as an efficient scheme to face various operational issues like reliability improvement and loss reduction. Furthermore, the potential for utilizing NR to perform voltage profile improvement under high DG or Renewable Energy Sources (RESs) penetration is presented. Finally, the coordination of the NR along with the optimal siting and sizing of DG units aiming to maximize their impact on loss reduction is also analyzed. The basic aim of this chapter is to demonstrate how specific automation upgrade in modern DNs regarding the replacement of manual switching equipment by automated controlled sectionalizers or tie-switches could allow Distribution System Operators (DSOs) to integrate real time management techniques of the DN under relatively low investment plans. Specific examples regarding both real and benchmarked DNs are included and the proposed algorithms are explained in detail.

Energy sector stakeholders, such as Distribution System Operators (DSO) or Aggregators take advantage of improved forecasting methods. Increased forecasting accuracy facilitates handling energy imbalances between generation and consumption. It also supports Smart Grid framework processes, such as Demand-Side or Demand Response Management (DRM). This paper presents a novel approach for One-Step-Ahead Energy Load Forecasting (OSA-ELF), considering several techniques. It utilizes historical data from a state-of-the-art nearly Zero Energy Building (nZEB) smart home, performing multiple tests for improved ELF. It focuses on OSA aspects of ELF, yet it can be utilized regardless of the time resolution. It predicts the “next step” value, regardless of the step’s duration (15-minutes, one-hour, one-day etc.) with high accuracy, and can be used for a wide variety of forecasting applications. To that end, fine-tuned ensemble methods and forecasting algorithms were utilized for experimenting with short term ELF. Forecasting evaluation produced good results with regards to popular accuracy (MAPE, SMAPE and RMSE) and an Execution Time (ET) metrics.

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.

As the evolution of new cutting-edge energy technologies gain more and more momentum, modern power systems require higher capability in controlling all the devices and equipment that are able to handle processes for smartening the typical usage of the energy resources. This is the case of Renewable Energy Sources and Distributed Generation, innovative energy storage systems, electromobility programs, etc. The inteGRIDy project aims to offer a unique approach and an integrated software solution for managing all the energy processes within a smart modern energy context, from the measurement and collection of data from the field, to the smart elaboration of the information related to these data, ending with the graphical representation and interface with the human actors interacting with this framework. The management of the data exchanged and data storage are handled as well. In this paper, the methodology adopted to design the inteGRIDy Reference Architecture and the layered structure of the Architecture itself are presented. All the different processes entailed by the management approach provided are described and detailed layer by layer, as their benefits for the energy system under control.

The penetration of Electric Vehicles (EVs) is rapidly increasing at the Low Voltage (LV) level, thus increasing the electrification rate of transport. Moreover, Photovoltaic (PV) systems at the LV level have been constantly increasing as well. Buildings with PVs on their roof and EVs as the main transport means may well become the norm the following years, considering additionally that all new buildings in the EU should be Nearly Zero Energy Buildings (NZEBs). Such NZEBs should cover the majority of their low energy demand by Renewable Energy Sources, and PV technology is the main candidate to achieve that. Both these facts pose considerable challenges on the LV networks. In this context, this paper examines the potential of efficiently utilizing the stored energy of Battery Energy Storage Systems (BESSs) in order to face the increased load demand by the penetration of EVs in LV Distribution Networks (DNs), with the presence of PVs. A Particle Swarm Optimization (PSO) algorithm is developed in order to perform optimal charging schedule for the EVs under the aim of optimizing the DN voltage profile. The analysis is performed on a real LV DN with real load data and the results indicate that under proper charging schedule both the voltage profile and the energy losses of the DN could be improved.

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

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 paper focuses on the optimal siting of Battery Energy Storage Systems (BESS) in a Distribution Network with installed Photovoltaic Generation, in order to minimize the energy losses of the system. The bus voltage limit, as well as the ampacity level of the lines are taken into consideration as constraints, while the technical constraints of the BESSs have also been taken into account. Unified Particle Swarm Optimization is used as the solving optimization technique. Simulations are being carried out on IEEE-33 bus system regarding different scenarios and the results are presented and compared. A significant improvement in energy losses, voltage and line ampacity profile is achieved by the introduction of BESS units in a Distribution Network with high PV Penetration.

This paper focuses on the optimal siting of Battery Energy Storage Systems (BESS) in a Distribution Network (DN) with installed Wind Generation (WG), in order to minimize the energy losses and reduce the power load fluctuation of the DN. The bus voltage limit, as well as the ampacity level of the lines are taken into consideration as constraints, while the technical characteristics of BESSs and WG have also been taken into account. In order to propose a solution for this optimization problem, the Unified version of the Particle Swarm Optimization technique is utilized, called UPSO. Simulations have been carried out on the typical 33-Bus system and the results are presented and compared. It is deduced that the introduction of BESSs can aid to the high penetration of WG, blunting the latter's severe consequences on the DN, reducing energy losses, and line ampacity levels and improving the voltage profile of the DN.

This paper examines voltage quality problems in Low Voltage (LV) Distribution Networks (DNs) due to high Photovoltaic (PV) and Electric Vehicles (EVs) penetration. The proposed methodology considers Battery Energy Storage Systems (BESSs) along with the PV units in order to store the energy surplus that causes overvoltage during the high PV generation. This stored energy is utilized at night when the majority of the EVs is expected to be charged. Moreover, a Unified Particle Swarm Optimization (UPSO) algorithm is used in order to optimally schedule EVs charging during the night and under different charging modes. This scheduling is implemented under either an assumed continuous charging mode or an intermittent one. The objective function is set to be the optimal voltage profile. A real LV DN with real measured load data is examined. Variations have been taken into account about some EVs' parameters and the results indicate that the voltage profile is significantly improved under the proposed charging schedule by the UPSO. If this charging schedule is combined with efficient exploitation of the BESS stored energy then the improvement is even higher. The proposed approach also improves energy efficiency towards the implementation of the Nearly Zero Energy Buildings (NZEB) concept.

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.

The increasing penetration of Renewable Energy Sources (RES) and generation uncertainties, brought to the fore new challenges and problems regarding efficient Distribution Networks (DNs) operation. Energy Storage Systems (ESS) can play a significant role in more reliable, secure and flexible DN operation since they can deal with difficult-to-predict changes. This study provides a detailed methodology among the corresponding mathematical formulation for the optimal sizing and allocation of ESS considering optimum operation schedule.

In this paper we present a new methodology for the formulation of efficient load signatures towards the implementation of a near-real time Non-Intrusive Load Monitoring (NILM) approach. The purpose of this work relies on defining representative current values regarding the 1st, 3rd and 5th harmonic orders to be utilized in the load signatures formulation. A measurement setup has been developed and steady-state measurements have been performed in a Low Voltage residence. A data processing methodology is proposed aiming to depict representative current values for each harmonic order in order to keep the load signature short and simple. In addition, a simple disaggregation scheme is proposed under linear equations for the disaggregation mode in order to examine the near-real time application of the methodology. The analysis indicates that the developed load signatures could be efficient for a per second application rate of the NILM algorithm. The results show that the higher harmonic currents facilitate the identification performance. Finally, the analysis concludes that for combinations that include appliances with intense harmonic content, the phase angle of the higher for harmonic currents should also be considered to the load signatures formulation.

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.