• Energy Research

The integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. This agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. This work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. The results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in the real-time pricing, scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead, and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.

Abstract This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users’ bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs.

The share of renewable energy sources (RESs) in the overall power production is on the upward trend in many power systems. Especially in recent years, considerable amounts of RES type distributed generations (DGs) are being integrated in distribution systems, albeit several challenges mainly induced by the intermittent nature of power productions using such resources. Optimal planning and efficient management of such resources is therefore highly necessary to alleviate their negative impacts, which increase with the penetration level. This paper deals with the optimal allocation (i.e. size and placement) of RES type DGs in coordination with reconfiguration of distribution systems (RDS). Moreover, the paper presents quantitative analysis with regards to the impacts of RDS on the integration level of such DGs in distribution systems. To this end, a tailor-made genetic algorithm (GA) based optimization model is developed. The proposed model is tested on a 16-node network system. Numerical results show the positive contributions of network reconfiguration on increasing the level of renewable DG penetration, and improving the overall performance of the system in terms of reduced costs and losses as well as a more stabilized voltage profile.

This work presents an extension of a second-order conic programming model (SOCP) to handle the multi-objective optimal power dispatch problem considering the probabilistic nature of some parameters related to power demand and the renewable energy sources (RES) generation, such as wind speed and solar irradiation level. Three objective functions are considered in this study: 1) costs of RES and non-RES generation; 2) active power losses in the transmission system; and, 3) emission pollutant gases produced by fossil fuel-based generating units. The stochastic nature of power demands and RES are developed through a set of representative operational scenarios extracted from historical data and via a scenario reduction technique. The results obtained in the SOCP model are compared with a nonlinear programming (NLP) model to check the robustness and precision of SOCP model. To this, both models are implemented and processed to simulate the optimal flow for the IEEE 57- and 118-bus systems.

La gestion optimale des ressources énergétiques distribuées (DER) et de la production basée sur les énergies renouvelables dans les systèmes multi-énergies (mes) est cruciale car on s'attend à ce que ces entités soient l'épine dorsale des futurs systèmes énergétiques. Pour gérer de manière optimale ces entités nombreuses et diversifiées, un agrégateur est nécessaire. Cet article propose l'auto-planification d'un agrégateur DER à travers une approche hybride info-gap Decision Theory (IGDT) -stochastique dans un mes. Dans cette approche, il existe plusieurs sources d'énergie renouvelables telles que les unités éoliennes et photovoltaïques (PV) ainsi que plusieurs DER, y compris les unités de cogénération (CHP) et les chaudières auxiliaires (AB). L'approche envisage également un parking pour véhicules électriques et des systèmes de stockage d'énergie thermique (tes). De plus, deux programmes de réponse à la demande (DR) à la fois basés sur les prix et basés sur les incitations sont utilisés dans le micro-réseau pour offrir de la flexibilité aux participants. L'incertitude dans la génération est abordée par la programmation stochastique. Dans le même temps, l'incertitude posée par les prix du marché de l'énergie est gérée par l'application de la méthode IGDT. Un objectif majeur de ce modèle est de choisir la mesure du risque en fonction de la nature et des caractéristiques des paramètres incertains dans le mes. De plus, le comportement des décideurs averses au risque et à la recherche de risques est également étudié. Dans la première étape, les résultats stochastiques uniques sont présentés, puis les résultats hybrides stochastiques-IGDT pour les décideurs à la fois averses au risque et demandeurs de risque sont discutés. Le problème proposé est simulé sur le système IEEE 15-bus modifié pour démontrer l'efficacité et l'utilité de la technique. La gestión óptima de los recursos energéticos distribuidos (der) y la generación basada en energías renovables en sistemas multienergéticos (mes) es crucial, ya que se espera que estas entidades sean la columna vertebral de los futuros sistemas energéticos. Para gestionar de manera óptima estas numerosas y diversas entidades, se requiere un agregador. Este documento propone la autoprogramación de un agregador DER a través de un enfoque híbrido de Teoría de Decisión de Info-gap (IGDT) -estocástico en un mes. En este enfoque, hay varios recursos de energía renovable, como unidades eólicas y fotovoltaicas (PV), así como múltiples der, incluidas unidades combinadas de calor y energía (CHP) y calderas auxiliares (AB). El enfoque también considera un estacionamiento EV y sistemas de almacenamiento de energía térmica (tes). Además, se emplean dos programas de respuesta a la demanda (DR) de categorías basadas en precios y en incentivos en la microrred para proporcionar flexibilidad a los participantes. La incertidumbre en la generación se aborda a través de la programación estocástica. Al mismo tiempo, la incertidumbre que suponen los precios del mercado energético se gestiona mediante la aplicación del método IGDT. Un objetivo importante de este modelo es elegir la medida de riesgo en función de la naturaleza y las características de los parámetros inciertos en el mes. Además, también se estudia el comportamiento de los tomadores de decisiones con aversión y búsqueda de riesgos. En la primera etapa, se presentan los resultados estocásticos únicos y luego, se discuten los resultados híbridos estocásticos-IGDT tanto para los tomadores de decisiones con aversión al riesgo como para los que buscan el riesgo. El problema propuesto se simula en el sistema IEEE 15-bus modificado para demostrar la efectividad y utilidad de la técnica. The optimal management of distributed energy resources (DERs) and renewable-based generation in multi-energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy systems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic programming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk-seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The proposed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique. تعد الإدارة المثلى لموارد الطاقة الموزعة (DERs) والتوليد القائم على الطاقة المتجددة في أنظمة الطاقة المتعددة (MESs) أمرًا بالغ الأهمية حيث من المتوقع أن تكون هذه الكيانات العمود الفقري لأنظمة الطاقة المستقبلية. لإدارة هذه الكيانات العديدة والمتنوعة على النحو الأمثل، يلزم وجود مجمع. تقترح هذه الورقة الجدولة الذاتية لمجمع DER من خلال نهج عشوائي لنظرية قرار فجوة المعلومات الهجينة (IGDT) في MES. في هذا النهج، هناك العديد من موارد الطاقة المتجددة مثل وحدات الرياح والطاقة الكهروضوئية بالإضافة إلى DERs متعددة، بما في ذلك وحدات الحرارة والطاقة المشتركة (CHP)، والمراجل المساعدة (ABs). كما يأخذ النهج في الاعتبار موقف السيارات الكهربائية وأنظمة تخزين الطاقة الحرارية (TESs). علاوة على ذلك، يتم استخدام برنامجين للاستجابة للطلب من كل من الفئات القائمة على الأسعار والقائمة على الحوافز في الشبكة المصغرة لتوفير المرونة للمشاركين. تتم معالجة عدم اليقين في الجيل من خلال البرمجة العشوائية. في الوقت نفسه، تتم إدارة عدم اليقين الذي تفرضه أسعار سوق الطاقة من خلال تطبيق طريقة IGDT. يتمثل الهدف الرئيسي لهذا النموذج في اختيار مقياس المخاطر بناءً على طبيعة وخصائص المعلمات غير المؤكدة في MES. بالإضافة إلى ذلك، تتم أيضًا دراسة سلوك صانعي القرار الذين يتجنبون المخاطرة ويبحثون عن المخاطرة. في المرحلة الأولى، يتم عرض النتائج العشوائية الوحيدة ثم تتم مناقشة النتائج العشوائية الهجينة لـ IGDT لكل من صانعي القرار الذين يتجنبون المخاطرة والباحثين عن المخاطرة. تتم محاكاة المشكلة المقترحة على نظام IEEE 15 - bus المعدل لإثبات فعالية وفائدة التقنية.

Due to the considerable increase of distributed energy resources, a new model of energy trading called peer- to-peer (P2P) has emerged in local energy communities that play a key role in the proliferation of renewable energy sources. However, although local and distributed power trading allows for a more decentralized and open grid, these models have a significant impact on the control, operation, and planning of the electricity distribution grid. Thus, reducing the demand for power at an affordable price is one of the main objectives of P2P markets, considering the different voltage limits and possible congestion existing in the distribution system. Thus, the main goal of this work is to evaluate the impact of the P2P market on the distribution network operation. This work includes an energy community in a neighborhood involving nine connected houses and one school, involving different renewable technologies and energy storage systems installed in each consumer and/or prosumer. The simulation results indicate that in the presence of local distributed generation and the inclusion of energy storage devices and electric vehicles allow a high-cost reduction (16%) and a very positive impact on the distribution system in terms of congestion and voltage deviations.

Distribution Systems (DS) are usually structured as weakly-meshed but the majority of them operate with a radial topology, mainly in order to accommodate the protection coordination. Obtaining the optimal radial configuration under several criteria has been an active research topic for more than two decades. Because of the computational burden and the non-linearity of the problem, the majority of the proposed methods and techniques, single or multi-objective, use various meta-heuristics. The DS reconfiguration problem, respecting the radiality constraints, is formulated in this paper as a multi- objective Mixed-Integer Linear Programming (MILP) problem. An adequate representation of the Pareto set is produced using an improved implementation of the e-constrained method. The objective is to determine the optimal radial configuration during several time intervals, minimizing the active power losses and the cost emerging from the switching operations. The proposed methodology is tested using a 16-node sample system.

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.