Browsing by Author "Castro, Rui"
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- 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.
- 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.
- Demand response implementation in smart householdsPublication . Fotouhi Ghazvini, Mohammad Ali; Soares, João; Abrishambaf, Omid; Castro, Rui; Vale, ZitaHome energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its’ effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%–31.5% by using the proposed optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.
- Dynamic Pricing for Demand Response Considering Market Price UncertaintyPublication . Fotouhi Ghazvini, Mohammad; Soares, João; Morais, Hugo; Castro, Rui; Vale, ZitaRetail energy providers (REPs) can employ different strategies such as offering demand response (DR) programs, participating in bilateral contracts, and employing self-generation distributed generation (DG) units to avoid financial losses in the volatile electricity markets. In this paper, the problem of setting dynamic retail sales price by a REP is addressed with a robust optimization technique. In the proposed model, the REP offers price-based DR programs while it faces uncertainties in the wholesale market price. The main contribution of this paper is using a robust optimization approach for setting the short-term dynamic retail rates for an asset-light REP.With this approach, the REP can decide how to participate in forward contracts and call options. They can also determine the optimal operation of the self-generation DG units. Several case studies have been carried out for a REP with 10,679 residential consumers. The deterministic approach and its robust counterpart are used to solve the problem. The results show that, with a slight decrease in the expected payoff, the REP can effectively protect itself against price variations. Offering time-variable retail rates also can increase the expected profit of the REPs.
- Engineering students education in sustainability: The moderating role of emotional intelligencePublication . Nogueira, Teresa; Castro, Rui; Magano, JoséIn the context of a lack of quantitative research approaching an engineering education in sustainability, this cross-sectional study aims to investigate whether efforts to promote sustainability education contribute to shaping the beliefs, attitudes, and intentions towards sustainability in a sample of Portuguese engineering schools students; in addition, this study investigates whether emotional intelligence impacts the students’ motivation to learn more about sustainability and whether it plays a role in moderating the relationships between those variables. A survey was carried out on a sample of 184 students from two major Portuguese engineering schools. A model was found showing that beliefs, attitudes, and gender are predictors of students’ intentions towards sustainability, explaining 62.6% of its variance. Furthermore, the findings reveal that women have stronger beliefs and intentions towards sustainability than men and that students with higher emotional intelligence are more motivated to learn more about sustainability. In addition, emotional intelligence has a negative and significant moderating impact on the relationship between attitudes and students’ intentions towards sustainability, being stronger for lower levels of emotional intelligence and having a similar, yet non-significant, effect on the relationship between beliefs and students’ intentions towards sustainability. The results suggest that emotional intelligence should be considered a competence and a tool in engineering education in order to enhance students’ inclination towards sustainable development.
- Evaluation of different initial solution algorithms to be used in theheuristics optimization to solve the energy resource scheduling insmart gridsPublication . Sousa, Tiago; Morais, Hugo; Castro, Rui; Vale, ZitaOver the last years, an increasing number of distributed resources have been connected to the powersystem due to the ambitious environmental targets, which resulted into a more complex operation ofthe power system. In the future, an even larger number of resources is expected to be coupled which willturn the day-ahead optimal resource scheduling problem into an even more difficult optimization prob-lem. Under these circumstances, metaheuristics can be used to address this optimization problem. Anadequate algorithm for generating a good initial solution can improve the metaheuristic’s performanceof finding a final solution near to the optimal than using a random initial solution. This paper proposestwo initial solution algorithms to be used by a metaheuristic technique (simulated annealing). Thesealgorithms are tested and evaluated with other published algorithms that obtain initial solution. Theproposed algorithms have been developed as modules to be more flexible their use by other metaheuris-tics than just simulated annealing. The simulated annealing with different initial solution algorithms hasbeen tested in a 37-bus distribution network with distributed resources, especially electric vehicles. Theproposed algorithms proved to present results very close to the optimal with a small difference between0.1%. A deterministic technique is used as comparison and it took around 26 h to obtain the optimal one.On the other hand, the simulated annealing was able of obtaining results around 1 min.
- 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.
- Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic AlgorithmPublication . Morais, Hugo; Sousa, Tiago; Castro, Rui; Vale, ZitaThe introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).
- A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailersPublication . Fotouhi Ghazvini, Mohammad Ali; Soares, João; Horta, Nuno; Neves, Rui; Castro, Rui; Vale, ZitaIn this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.
- A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid contextPublication . Sousa, Tiago; Morais, Hugo; Vale, Zita; Castro, RuiIn the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.