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Flexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual., This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project PRECISE (PTDC/EEI-EEE/6277/2020), and CEECIND/01423/ 2021. The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.

Demand response is a very flexible way to improve distributed energy resources scheduling. The innovative contribution of this paper is to include in the model complex contracts approach, which can accommodate constraints according to the particular expectations of each player. Such contracts are included in the distributed energy resources scheduling optimization to dispatch demand response according to the consumers expectations. Multi­period demand response events are considered. In this way, consumers can specify limits on the time, power, and remuneration regarding participation in demand response events, in a way that has not been considered in the literature. The state of the art treats these aspects separately or using a statistic approach, without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment. The model has been validated for 218 consumers using several scenarios and different types of distributed generation, showing that it is possible to increase demand response respecting consumers preferences., This work has received funding from the COLORS Project, from FEDER Funds through COMPETE Program, and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.

Demand response (DR) and its advantages are nowadays unquestionable due to the success of several recent implementations of DR programs. Improved methodologies and approaches are needed for the adequate consumers’ schedule in DR events, taking the consumers’ behaviour and preferences into account. In this paper, a virtual power player manages DR programs, minimizing operation costs, respecting the consumption shifting constraints. The impact of the consumption shifting in the target periods is taken into consideration. The advantages of the DR use in comparison with distributed generation (DG) are evaluated. Two scenarios based on 218 consumers in a frame of 96 periods have been implemented. It is demonstrated the advantages of DR in the operation of distributed energy resources, namely when considering the lack of supply., The present work was done and funded in the scope of CONTEST Project, and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.

The growing number of distributed energy resources in power systems, leads to the appearance of new entities and roles for the existing ones that affect the operation of the network. One of these entities with more relevance, is the aggregator, either independent or represented by public organizations. An aggregator manages small-size distributed energy resources, creating a virtual amount of energy flexibility that can be used by it, to enable participation in energy markets and capitalize the integration of distributed energy resources. This paper proposes a two-stage optimization methodology for the operation of an aggregator regarding distributed energy resources. In a first stage, the network part managed by the aggregator is scheduled, meaning at a macro perspective, while in the second stage, it is assumed that the distributed energy resources are also scheduled considering their operation. It is assumed that this second stage is enabled due to an aggregator’s communication infrastructure and interconnected management systems.

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific programs in the LMP values supports the manager decision concerning the DR use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 33-bus network with intensive use of DG.

The recursive implementation of smart grid measures in the current energy systems, introduces demand-side resources to new concepts that require their active participation and management. However, these management systems often don’t consider a human factor, i.e. the features that effect the personal comfort of each person, e.g. the lighting, air conditioning, appliances in a given space. In this way, the present paper proposes a methodology for dealing with locational sensor data, to perform the implementation of demand response programs. At the same time, the methodology addresses the scheduling of distributed generation and external suppliers to balance the consumer’s load. The proposed methodology is tested on a case study involving the management of an office building, adapted to simulated sensor data.

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significan t part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with ade quate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Energy policies have been widely developed in the recent past as sequence of the increasing relevance of distributed energy resources potential in power systems, namely in achieving the reduction of carbon dioxide emissions gaining independence from fossil fuels. Thus, the main factions in the world, as North America and Europe, have been focusing on the implementation of new energy policies capable of managing several types of energy sources considering their decentralized characteristics. In this way, the present work provides an introduction of how the new energy policies, concerning distributed energy resources, are working towards the increase of these resources penetration in the energy mix. Some successful case studies are presented, namely from Europe, to assess the benefits of such policies to consumers, to producers and to energy market as a whole.

The need to increase the share of renewable energy resources in several countries around the world led to the development of new strategies, in order to implement more effective energy policies. These resources have a distributed nature and are one of the main paths to incentive the reduction of CO2 emissions, and their impact on the environment. Many countries are making efforts to advance in the implementation of their own strategies to achieve the fossil fuels independency. Therefore, the intent is to stipulate energy policies that increase renewable energy share in the energy mix. These policies depend on regulations, taxation, incentives and promotional schemes. In this paper, it is presented a brief introduction to the energy policies in two countries, Denmark and Finland. Demand Response (DR) aspects and its impact in the energy market are also discussed.

The growing number of distributed energy resources in power systems, leads to the appearance of new entities and roles for the existing ones that affect the operation of the network. One of these entities with more relevance, is the aggregator, either independent or represented by public organizations. An aggregator manages small-size distributed energy resources, creating a virtual amount of energy flexibility that can be used by it, to enable participation in energy markets and capitalize the integration of distributed energy resources. This paper proposes a two-stage optimization methodology for the operation of an aggregator regarding distributed energy resources. In a first stage, the network part managed by the aggregator is scheduled, meaning at a macro perspective, while in the second stage, it is assumed that the distributed energy resources are also scheduled considering their operation. It is assumed that this second stage is enabled due to an aggregator’s communication infrastructure and interconnected management systems., This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and from FEDER Funds through COMPETE program and from National Funds through FCT, under the project UID/EEA/00760/2013.

The need to increase the share of renewable energy resources in several countries around the world led to the development of new strategies, in order to implement more effective energy policies. These resources have a distributed nature and are one of the main paths to incentive the reduction of CO2 emissions, and their impact on the environment. Many countries are making efforts to advance in the implementation of their own strategies to achieve the fossil fuels independency. Therefore, the intent is to stipulate energy policies that increase renewable energy share in the energy mix. These policies depend on regulations, taxation, incentives and promotional schemes. In this paper, it is presented a brief introduction to the energy policies in two countries, Denmark and Finland. Demand Response (DR) aspects and its impact in the energy market are also discussed.

There are currently efforts to implement the concept of smart grids throughout the electric sector. This will bring radical changes to the entire management of the sector. The energy market does not run away from the rule. In this way, virtual power players will be required to update their business models to introduce all the concepts that the context of smart grids imposes. Thus, in this article is proposed a method that aggregates distributed generation and consumers who belong to demand response programs. Optimized scheduling, resource aggregation and classification of possible new resources, rescheduling, and remuneration are the phases of the methodology proposed and presented in this article. The focus will be on classification phase and the main objective is to create rules, through a previously trained model, to be able to classify the new resources and help with the challenges that virtual power players may face. Thus, five classification methods were tested and compared: neural networks, Bayesian naïve classification, decision trees, k-nearest neighbor method, and lastly support vector machine method.

The use of demand response programs and distributed renewable energy resources play an important role in nowadays electricity markets. Most of the demand response programs are performed for large-scale resources. This is a barrier for the small and medium scale consumers, producers, and prosumers in order to participate in the electricity market negotiations. To overcome this barrier, a third-party entity, such as community or an aggregator, should play a role an intermediate player between the end-users and network operators. However, before the implementation of the business models, they should be well surveyed in term of economic and financial profits in order to prevent future problems. This paper proposes an economic survey on a community of the consumers and distributed generations, considering different pricing schemes. The community consists of residential, commercial, and industrial consumers as well as photovoltaic and wind turbines. In this survey, the annual costs of this community are investigated considering the current pricing schemes in two countries of Portugal and Germany., UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.

The recursive implementation of smart grid measures in the current energy systems, introduces demand-side resources to new concepts that require their active participation and management. However, these management systems often don’t consider a human factor, i.e. the features that effect the personal comfort of each person, e.g. the lighting, air conditioning, appliances in a given space. In this way, the present paper proposes a methodology for dealing with locational sensor data, to perform the implementation of demand response programs. At the same time, the methodology addresses the scheduling of distributed generation and external suppliers to balance the consumer’s load. The proposed methodology is tested on a case study involving the management of an office building, adapted to simulated sensor data., The present work was done and funded in the scope of the following project: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794).

Energy policies have been widely developed in the recent past as sequence of the increasing relevance of distributed energy resources potential in power systems, namely in achieving the reduction of carbon dioxide emissions gaining independence from fossil fuels. Thus, the main factions in the world, as North America and Europe, have been focusing on the implementation of new energy policies capable of managing several types of energy sources considering their decentralized characteristics. In this way, the present work provides an introduction of how the new energy policies, concerning distributed energy resources, are working towards the increase of these resources penetration in the energy mix. Some successful case studies are presented, namely from Europe, to assess the benefits of such policies to consumers, to producers and to energy market as a whole.