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- Revenue management prlcing in Douro Hotels [Study Group Report]Publication . Correia, Elisete; Dias, Sónia; Ferreira, Fernanda A.; Moura, Ana; Nunes, Paula; Pereira, Maria Teresa Ribeiro; Santos, Jorge; Pereira, Carla Santos; Soares, Filomena Baptista; Soares, JoãoThe Douro Palace Hotel management wants to develop a software that helps them with the dynamic pricing of the hotel rooms. Revenue Management software is widely used for data analysis of demand and prices. However the hotel barely follows the software recommendations for pricing decisions, mainly due to the lack of understanding of the rationale and the inherent difficulty in validating pricing decisions. The hotel would like to have a reliable model in order to give to the manager an overview of the business and how to act to improve the sales. The questions raised at the study group are related to this. In this report, we present two distinct methodologies to address the hotel request. The first approach is excel based and more practically oriented towards to be an immediate help of the current pricing decisions. A second approach is more formal, mathematically speaking, but requires a more time consuming implementation. It is based on a recent operations management/operations research paper. On short term plan, we propose the implementation of an automatic generation of excel files that will enable Douro Palace Hotel react more often/finely to the competition pricing. A thorough statistical analysis of the Demand function and the corresponding segmentation is also required - for simulation purposes or decision support. We think that Douro Palace Hotel should differentiate prices more often and promptly (both upwardly and downwardly) depending on the perception of the demand and the competitors prices. On a long term plan, we propose the implementation of more sophisticated approaches, mathematically speaking, such as the one described in this report.
- Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systemsPublication . Canizes, Bruno; Soares, João; Vale, Zita; Khodr, H. M.This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.
- Optimal methodology for renewable energy dispatching in islanded operationPublication . Khodr, H. M.; Vale, Zita; Ramos, Carlos; Soares, João; Morais, H.; Kadar, PeterThe management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.
- Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-gridPublication . Soares, João; Sousa, Tiago; Morais, Hugo; Vale, Zita; Canizes, Bruno; Silva, António S.This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.
- Safety Isolating Transformer Design using HyDE-DF algorithmPublication . Soares, João; Lezama, Fernando; Vale, Zita; Brisset, Stephane; Francois, BrunoThis paper presents an application of Evolutionary Computation (EC) to the benchmark of the safety isolating transformer problem. The benchmark adopts multidisciplinary optimization strategies, namely the multidisciplinary feasible (MDF) and the individual discipline feasible (IDF) formulations. The benchmark meets the requirements of engineers and scientists working with machine design problem, such as in the first part of the design process that is the choice of structure and materials. The EC methods employed in this paper are based on Evolutionary Algorithms (EAs), namely two variants of Differential Evolution (DE), two variants of Hybrid Adaptive DE (HyDE) and the Vortex Search (VS). The results showed in this paper suggest that EA methods are competitive with the classical optimization method, the sequential quadratic programming (SQP). Among the developed EAs, HyDE-DF is able to obtain better values than SQP on a significant battery of trials.
- Benders’ Decomposition Applied to Energy Resource Management in Smart Distribution NetworksPublication . Soares, João; Canizes, Bruno; Vale, ZitaThis paper proposes a Benders’ decomposition approach for the large-scale Energy Resource Management (ERM). The problem considers large amount of Distributed Energy Resources (DER) including Electric Vehicles (EVs) with gridable capacity. The proposed model is designed for Virtual Power Players/Plants (VPPs) managing a smart distribution network with the aim to maximize profits considering the dayahead time horizon. Previous literature has shown difficulties in solving similar problems using classical techniques in a centralized scheme, namely considering nonlinear network constrains. The proposed approach reduces computational burden of the original Mixed Integer Nonlinear Programming (MINLP) problem. The Benders’ cuts are introduced for the ERM formulation in order to allow the communication between the slave and the master problem. Analysis of two large-scale instances have been carried out for smart distribution networks, namely a 33-bus and a real Portuguese 180-bus network assuming high penetration of DERs and EVs.
- Using data mining techniques to support DR programs definition in smart gridsPublication . Vale, Zita; Morais, H.; Ramos, Sérgio; Soares, João; Faria, PedroIn recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.
- A Mixed Binary Linear Programming Model for Optimal Energy Management of Smart BuildingsPublication . Foroozandeh, Zahra; Ramos, Sérgio; Soares, João; Lezama, Fernando; Vale, Zita; Gomes, António; Joench, Rodrigo L.Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs.
- Multi-objective robust optimization to solve energy scheduling in buildings under uncertaintyPublication . Soares, João; Vale, Zita; Borges, Nuno; Lezama, Fernando; Kagan, NelsonWith the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO 2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO.
- Stochastic interval-based optimal offering model for residential energy management systems by household ownersPublication . Shokri Gazafroudi, Amin; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Pinto, Tiago; Vale, Zita; Corchado, Juan ManuelThis paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.