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- Decision Support for Negotiations among Microgrids Using a Multiagent ArchitecturePublication . Pinto, Tiago; Fotouhi Ghazvini, Mohammad Ali; Soares, João; Faia, Ricardo; Corchado, Juan; Castro, Rui; Vale, ZitaThis paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results.
- Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple AggregatorsPublication . Almeida, José; Soares, João; Canizes, Bruno; Lezama, Fernando; Fotouhi Ghazvini, Mohammad Ali; Vale, ZitaThe ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.
- Liberalization and customer behavior in the Portuguese residential retail electricity marketPublication . Fotouhi Ghazvini, Mohammad Ali; Ramos, Sérgio; Soares, João; Castro, Rui; Vale, ZitaThe final step that Portugal is taking to reach a fully liberalized electricity market is the deregulation of the retail market by phasing-out regulated electricity prices and reducing the administrative burdens in this area. These attempts are done to promote the entrance of companies into the retailing business and to actively engage the end-users in the market. This analysis shows that despite high consumer switching rates during the 2013–2015 period, the retail market in Portugal is still highly concentrated. The retail rates are also not following the changes in the wholesale market price.
- Long-Term Smart Grid Planning Under Uncertainty Considering Reliability IndexesPublication . Canizes, Bruno; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Silva, Cátia; Vale, Zita; Corchado, Juan M.The electricity sector is fast moving towards a new era of clean generation devices dispersed along the network. On one hand, this will largely contribute to achieve the multi-national environment goals agreed via political means. On the other hand, network operators face new complexities and challenges regarding network planning due to the large uncertainties associated with renewable generation and electric vehicles integration. In addition, due to new technologies such as combined heat and power (CHP), the district heat demand is considered in the long-term planning problem. The 13-bus medium voltage network is evaluated considering the possibility of CHP units but also without. Results demonstrate that CHP, together with heat-only boiler units, can supply the district heat demand and contribute to network reliability. They can also reduce the expected energy not supplied and the power losses cost, avoiding the need to invest in new power lines for the considered lifetime project.
- Local Electricity Markets for Electric Vehicles: An Application Study Using a Decentralized Iterative ApproachPublication . Faia, Ricardo; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Franco, John F.; Vale, ZitaLocal electricity markets are emerging solutions to enable local energy trade for the end users and provide grid support services when required. Various models of local electricity markets (LEMs) have been proposed in the literature. The peer-to-peer market model appears as a promising structure among the proposed models. The peer-to-peer market structure enables electricity transactions between the players in a local energy system at a lower cost. It promotes the production from the small low–carbon generation technologies. Energy communities can be the ideal place to implement local electricity markets as they are designed to allow for larger growth of renewable energy and electric vehicles, while benefiting from local transactions. In this context, a LEM model is proposed considering an energy community with high penetration of electric vehicles in which prosumer-to-vehicle (P2V) transactions are possible. Each member of the energy community can buy electricity from the retailer or other members and sell electricity. The problem is modeled as a mixed-integer linear programing (MILP) formulation and solved within a decentralized and iterative process. The decentralized implementation provides acceptable solutions with a reasonable execution time, while the centralized implementation usually gives an optimal solution at the expense of reduced scalability. Preliminary results indicate that there are advantages for EVs as participants of the LEM, and the proposed implementation ensures an optimal solution in an acceptable execution time. Moreover, P2V transactions benefit the local distribution grid and the energy community.
- Congestion management in active distribution networks through demand response implementationPublication . Fotouhi Ghazvini, Mohammad Ali; Lipari, Gianluca; Pau, Marco; Ponci, Ferdinanda; Monti, Antonello; Soares, João; Castro, Rui; Vale, ZitaDespite the positive contributions of controllable electric loads such as electric vehicles (EV) and heat pumps (HP) in providing demand-side flexibility, uncoordinated operation of these loads may lead to congestions at distribution networks. This paper aims to propose a market-based mechanism to alleviate distribution network congestions through a centralized coordinated home energy management system (HEMS). In this model, the distribution system operator (DSO) implements dynamic tariffs (DT) and daily power-based network tariffs (DPT) to manage congestions induced by EVs and HPs. In this framework, the HP and EV loads are directly controlled by the retail electricity provider (REP). As DT and DPT price signals target the aggregated nodal demand, the individual uncoordinated HEMS models operating under these price signals are unable to effectively alleviate congestion. A large number of flexible residential customers with EV and HP loads are modeled in this paper, and the REP schedules the consumption based on the comfort preferences of the customers through HEMS. The effectiveness of the market-based concept in managing the congestion is demonstrated by using the IEEE 33-bus distribution system with 706 residential customers. The case study results show that considering both pricing systems can considerably mitigate the overloading occurrences in distribution lines, while applying DTs without considering DPTs may lead to severe overloading occurrences at some periods.
- Application of a Home Energy Management System for Incentive-Based Demand Response Program ImplementationPublication . Abrishambaf, Omid; Fotouhi Ghazvini, Mohammad Ali; Gomes, Luis; Faria, Pedro; Vale, Zita; Corchado, Juan M.This paper presents an experimental real-time implementation of an incentive-based demand response program with hardware demonstration of a home energy management system. This system controls the electricity consumption of a residential electricity customer. For this purpose, the real consumption and generation profiles of a typical Portuguese household equipped with a home-scale photovoltaic system are employed. These profiles are simulated by the real-time digital simulator using real hardware resources. In the case studies, three different scenarios are simulated for a period of 24 hours with the consideration of the demand response programs and a 2 kW photovoltaic system. Different pricing scenarios are considered and the performance of the home energy management system is evaluated under each scenario. The focus is given to demonstrate how a home-scale photovoltaic system, and demand response programs, especially load-shifting scenario, can be cost-effective in the daily electricity costs of the residential customers.
- Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Smart GridsPublication . Soares, João; Lezama, Fernando; Canizes, Bruno; Fotouhi Ghazvini, Mohammad Ali; Vale, Zita; Pinto, TiagoThe integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the day-ahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model