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The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

The use of data from residential smart meters can help in the management and control of distribution grids. This provides significant benefits to electricity retailers as well as distribution system operators but raises important questions related to the privacy of consumers' information. In this article, an innovative differential privacy (DP) compliant algorithm is developed to ensure that the data from consumer's smart meters are protected. The effects of this novel algorithm on the operation of the distribution grid are thoroughly investigated not only from a consumer's electricity bill point of view but also from a power systems point of view. This method allows for an empirical investigation into the losses, power quality issues, and extra costs that such a privacy-preserving mechanism may introduce to the system. In addition, severalcost allocation mechanisms based on the cooperative game theory are used to ensure that the extra costs are divided among the participants in a fair, efficient, and equitable manner. Overall, the comprehensive results show that the approach provides privacy preservation in line with the consumer's preferences and does not lead to significant cost or loss increases for the energy retailer. In addition, the novel algorithm is computationally efficient and performs very well with a large number of consumers, thus demonstrating its scalability.

There is an urgent need to reduce the combustion of fossil fuels and replace these sources with renewable energy sources. The two major renewable energy resources, solar PV and wind generation, are variable. This variability makes balancing the electrical system more difficult. One way to manage this volatile system is to use markets for ancillary services to ensure that the electrical grid can operate in a safe, efficient and reliable manner. This paper proposes a methodology for a group of smaller consumers to be aggregated together so that they can effectively bid into markets for ancillary services. The methodology is tested on the Portuguese reserve regulation market and the financial viability of such aggregation is explored. Results show that aggregating consumer bids for downward regulation services can be financially viable in the Portuguese market. Reducing the minimum bid size increased the participation of the consumers thus increasing revenues.

Nowadays demand response (DR) is known as one of the main parts of the power system especially in the smart grid infrastructure. Furthermore, to enhance the participation of the consumers in the DR programs, the Independent System Operators (ISOs) have introduced a new entity, i.e. Demand Response Aggregator (DRA). The main contribution of this paper is to investigate a novel framework to increase the profits of the DRA participating in the day-ahead electricity market, i.e. employment of an axillary generation system in the DRA entity. It is supposed that the DRA in this paper has an axillary generation system and it would lead to an increase in the profit of the DRA through avoiding the economic loss in the process of trading DR obtained by the active participation of prosumers in the electricity market. The fuel cell is introduced as the axillary generation unit to the DRA unit. In the framework proposed in this paper, the DR is acquired from end-users during peak periods and will be offered to the day-ahead electricity market. The power flow during the off-peak hours is in another direction, i.e. from the grid to the consumers. In this model, the information-gap decision theory (IGDT) is chosen as the risk measure. The uncertain parameter is the day-ahead electricity market price. The optimization problem’s objective is to maximize the profit of the DRA. The behavior of the risk-seeker decision-maker is analyzed and investigated. The feasibility of the program is demonstrated by applying it to realistic data.

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.

Due to technological advances, the growing need for a decarbonized economy, and the desire to reduce urban air pollution, electric vehicles (EVs) are seen as promising developments for the progressive decarbonization of the transport sector. The potential for large scale integration of solar photovoltaic (PV) systems has not been explored fully, compared to other renewable energy uses in power systems. One of the proposed paths for the sustainable integration of EVs and solar exploitation in power systems involves the creation or installation of parking lots fitted with solar PV systems on their rooftops. This concept increases the reliability and robustness of the power system, as studies show that EVs are parked for the vast majority of the time. The EVs can then be optimally managed to assist the network in critical moments. The solar parking lot would then serve as a backup to manage the EVs' state-of-charge (SoC) which would guarantee the owners’ comfort and help to accelerate the decarbonization of the economy. The present work presents a comprehensive survey of the state-of-the-art concepts of photovoltaic (PV) panels, EVs and batteries, and how the different associated technologies can be applied in the concept of solar parking lots.

Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The combination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is presented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator.

This paper focuses on the long-term planning of power systems considering the impacts of Electrical Energy Storage Devices (ESSD) as well as Demand Response Programs (DRPs). The proposed model incorporates a two-stage optimization strategy in order to reduce the computational burden of the nonlinear problem. The upper-level of optimization model includes investment decision variables (long-term planning) while in the lower-level, the optimal operation of the model for short-term horizon has been addressed. In the operational stage, the optimal scheduling of power system in the presence of suggested ESSD size and location from the upper level is evaluated. Moreover, the Time-of-Use (ToU) Demand Response (DR) pricing scheme has been applied in the operational stage to evaluate its capability to reduce the total operating costs. The simulation results on the standard 6-bus test system validates the applicability of the proposed two-stage optimization model and illustrates that the optimal sizing and location of ESSDs along with DRP implementation could effectively reduce the total systems costs and improve the power system load factor.

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS© Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed.