Abstract
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

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 200euro/tCO2.

Abstract

Abstract

Abstract
The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.