Search

This research proposes an investigation on the problem of detecting and predicting occupancy by using solely readily available electricity consumption data, obtained from smart metres. The following research questions are defined: (1) Is it possible to predict occupancy by using solely electricity consumption data?; (2) Is it possible to use a single classification model to monitor occupancy in multiple households? The findings show that an occupancy detection accuracy of up to 92% can be achieved by using solely electricity consumption data. The problem of generalizing the classification model, i.e. using a single classification model to monitor occupancy in multiple households, is also addressed. It is found that an occupancy detection accuracy of up to 83% is achievable in this case. Regarding occupancy prediction, occupancy in multiple households with an accuracy of up to 75% is obtained, by using solely electricity consumption data. For both occupancy monitoring and prediction, it is found that households with a low level of occupancy can benefit more from these systems. [ABSTRACT FROM AUTHOR]

As energy system will increasingly become renewable, they will face new challenges that mainly arise due to the varying output by many renewable energy technologies. An issue that is especially prominent for countries with high shares of hydropower in their electricity generation is the varying yearly output of these technologies. This work aims to propose a techno-economically optimized energy system for such a country, using Portugal as example. For this purpose the current situation is analyzed and an energy demand model for 2050 created. The energy system is optimized for years with different outputs of hydropower to analyze the system's behavior using the simulation tool EnergyPLAN. As the tool does not possess inherent optimization capabilities it was coupled with the gray wolf optimization algorithm using Matlab. Subsequently, a system is proposed that is able to cover Portugal's expected energy demand under all circumstances. The future system will rely strongly on wind and solar power as they are expected to cover around 75% of Portugal's electricity demand. Furthermore, the analysis showed that Portugal needs to build up electrolyzer capacities of 4.2 GW and SynGas capacities of 2.4 GW as the SynGas will be used to balance supply in demand, especially in years with lower hydropower output. The system's cost will be between 22 and 35% cheaper than the created reference model. Furthermore, the primary energy demand is expected to decrease from 253 TWh to around 150 TWh while the electricity demand rises from 49 TWh to around 110 TWh. • We propose an optimized 100% renewable energy system for countries with high shares of hydropower. • The proposed system can cover Portugal's expected energy demand in 2050. • Wind and solar power are expected to cover around 75% of Portugal's electricity demand. • Portugal needs to build up electrolyzer capacities of 4.2 GW and SynGas capacities of 2.4 GW. • The system's cost will be between 22 and 35% cheaper than the created reference model. [ABSTRACT FROM AUTHOR]

Retail 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. [ABSTRACT FROM AUTHOR]

Poland is facing demanding challenges to achieve a sustainable energy mix in the near future. Crucial and tough decisions must be made about the direction of the national energy economy, safety, and environmental impact. Considering the electricity and heating demand forecast, this paper proposes an optimization model based on the Grey Wolf Optimizer meta-heuristic to support the definition of ideal energy mix considering the investment and operational costs. The proposed methodology uses the present energy mix in Poland (the most recent values are from 2017) to calibrate the model implemented in the EnergyPLAN tool. Afterwards, EnergyPLAN relates to an optimization process allowing the identification of the most convenient energy mix in 2040 in Poland. The values obtained are compared with those proposed by Polish public entities showing advantage regarding the global costs of the project nevertheless respecting the same levels of CO2 and the energy import and export balance. The expected savings can achieve 1.3 billion euros a year and more than 8 million tonnes of CO2 emission reduction. Sensitivity analysis considering the decrease of the global cost of renewables-based sources is also presented. [ABSTRACT FROM AUTHOR]

• Design of a non-intrusive device to intelligently control electric water heaters. • Development of a machine-learning tool to forecast residential hot water demand. • Details and results of a six-month pilot study where the device was implemented. • Heating demand reduced by an average of 26.43% and cost saving of 35.54% per device. • Device is cost-effective and can reduce the need for imported fossil-fuel generation. Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer's hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative 'heat now' function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO 2 per year. The results from the consumer survey show that the device did not affect the consumer's comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]