• Energy Research

The main objective of this paper is to present a DAB protocol and a DAB grid agent prototype, as essential parts of a grid ancillary services operational model. The decentralized, small electric loads (electric vehicles, household-appliances, batteries, prosumers), clustered properly by aggregators are equipped in large amounts with the so called “grid-agents”. A grid-agent is a cost-effective, low-power, smart electric control unit consisting of a DAB+ receiver with embedded circuits for local grid voltage and frequency measurements and a microcontroller running algorithms for flexible load control. The grid-agent will optimize the electricity demand with respect to the grid status and/or to external commands received via DAB+. Moreover, especially suited for Transmission System Operators (TSOs), multi-load power facilities that are controlled by energy management systems through specific standards (e.g. KNX for hotels, Modbus for the industrial sector, etc) and even Renewable Energy Sources (RES) Distributed Generators (DGs) are equipped with a single DAB+ grid-agent.

Abstract Around the world, there is a great concern with the emission of greenhouse gases, creating great interest in turning the energy systems more sustainable. Multi-energy systems are considered as a potential solution to help to this cause and in recent years, it has gained much attention from both research and industry. In this paper, an optimization model is proposed to use the flexibility of multi-energy systems to mitigate the uncertainty associated with wind generation. The differences between the flexibility provided by multi-energy systems and electrical storage systems in the network were studied. The results prove that the flexibility of the multi-energy systems can benefit the system in several aspects and provide insights on which is the best approach to take full advantage of renewable resources even when a high degree of uncertainty is present.

This paper describes technical solutions to be adopted by Electric Vehicles (EV) battery grid interfaces, in order to get the provision of ancillary services to the grid. The developed solution exploits an EV battery charge control approach based in a grid cooperative response to frequency and/or voltage deviations. To be effective, those cooperative actions must result from both centralized/coordinated commands and local/individual EV charger response to the grid behaviour. In a scenario characterized by a massive deployment of EV, the adoption of such a solution allows an improvement on the power system dynamic behaviour, namely in small island grids.

Abstract — Stationary batteries are currently seen as an interesting solution to deal with the variability of the renewable energy sources. In the same way as other types of storage, e.g. pumped-hydro units, this new type of storage equipment can improve the use of Renewable Energy Sources (RES). Additionally, the stationary batteries location in the grid is not as physically constrained as other storage systems and can be optimally selected to maximize its overall benefits. This paper proposes a new methodology to represent the unique stochastic behavior of stationary batteries while integrated into an electrical power system. This methodology includes not only the technical restrictions of this type of storage system but also how its operation strategy affects its lifetime. The methodology was tested on a small test system, which is based on the IEEE-RTS 79, using sequential Monte Carlo simulation as its core to accurately reproduce the chronology of events of stationary batteries. The results of the simulation are focused on the potential impacts of these storage devices not only in terms of renewable energy used but also in the adequacy of supply.

Abstract This paper proposes a two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets. The aggregator optimizes the prosumers’ flexibility with the objective of minimizing the net cost of buying and selling energy and secondary reserve in both day-ahead and real-time market stages. The uncertainties of the renewable generation, consumption, outdoor temperature, prosumers’ preferences, and house occupancy are modeled through a set of scenarios. For a case study of 1000 prosumers, the results show that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.

The smart home will bring many challenges. One of the challenges is how to design a smart home that satisfies the needs of the residents in a cost-effective way. This paper addresses this challenge by proposing an optimization model to define the optimal portfolio of smart home technologies and electricity tariffs that minimize the overall investment and operation costs of the house owner. The smart home technologies include electric vehicle charging stations, battery energy storage systems, home energy management systems, and photovoltaic systems. A case study of a real house in Portugal was used to evaluate the performance of the planning optimization model. The numerical results show that the optimization model selects the combination of smart home technologies and electricity tariffs that best meets the needs of the household owner in a cost-effective way.

Abstract The increasing replacement of conventional generators by variable renewable energy sources is reducing the flexibility of the power system, and consequently reducing its reliability indexes. To compensate for this reduction of flexibility, market participation of aggregators of multi-energy systems has been proposed in the literature. Under this scope, this paper presents a network-secure bidding optimization strategy to assist aggregators of multi-energy systems calculating electricity (energy and reserve), gas and carbon bids, considering multi-energy network constraints. This strategy is a distributed approach based on the alternating direction method of multipliers, where the aggregator collaborates with the operators of electricity, gas and heat networks to calculate network-secure bids. The proposed strategy is benchmarked against two other approaches. The results show that the newly developed strategy computes multi-energy and network-secure bids with execution times that suit the timelines of the electricity, gas, and carbon markets. The joint optimization of multi-energy systems reduced the aggregator’s costs by 89% compared to a single energy-vector approach. Furthermore, two sensibility studies were also performed. The first study revealed that in the presence of slow ramp-rate resources (e.g. combined heat and power systems), aggregator’s costs can decrease up to 87% when considering slower response times to the secondary reserve signal. In the second study, it was observed that the bidding behavior of the aggregator only starts changing significantly with carbon prices higher than 200€/tCO2.

The multi-energy systems (MES) contain key resources driving the evolution of the future systems. Various components and convertors that are available in a MES make it operationally flexible and a potential source to be deployed in system operation. Like any other resources in the system, the flexibility brought by MES needs to be fairly valued. One of the approaches is through market participation of these resources. In this regard, new agents and trade system need to be defined. This paper studies the interactions of a multi-energy aggregator on various trade levels defined within the multi-energy paradigm. The levels include the upstream multi-energy markets as well as local energy trades such as local resources and flexible demand. The results discuss the increased level of profit due to the availability of multi-energy trade to the aggregator.