Demand response has proven to be a distributed energy resource of great potential over the last decades for electrical systems operation. However, small or medium size facilities generally have a ...very limited ability to participate in demand response programs. When a facility includes several generation resources, energy storage systems, or even demand flexibility, the decision-making becomes considerably harder because of the amount of variables to be considered. This paper presents a method to facilitate end users' decision-making in demand response participation. The method consists of an algorithm that uses demand and generation forecasts and costs of the available resources. Depending on the energy to be reduced in a program, the algorithm obtains the optimal schedule and facilitates decision making, helping end users to decide when and how to participate. With this method, end users' capability to participate in these programs is clearly increased. In addition, the method is contrasted by simulations based on real programs developed at the Campus de Vera of the Universitat Politècnica de València. The simulations carried out show that the developed method allows end users to take advantage of the potential of their facilities to provide demand response services and obtain the maximum possible benefit.
•A method to facilitate decision-making in demand response is proposed.•The proposed algorithm enables facilities to participate in demand response programs.•The proposed method maximises the benefits that can be obtained with demand response.•A method to optimise demand response participation is tested.•Participation in demand response with distributed energy resources is facilitated.
In European countries, an energy transition towards renewable energies is taking place, promoting self-consumption photovoltaic systems. Some studies point out that the existing regulations and ...support for photovoltaic systems is still scarce in Spain. This work analyses the penetration of photovoltaic systems and their amortisation in rural areas within the Spanish regulatory framework. For this purpose, an economic decision-making method for the implementation of photovoltaic systems is proposed. This method is based on comparing the possible self-consumption scenarios included in the Spanish regulation and their payback periods. Subsequently, a rural municipality in Spain has been analysed in detail as a case study. Results show that the Spanish regulation on self-consumption does not provide an adequate profitability in rural areas, as the amortisation period with surplus sale is between 16 and 22 years. If the surplus generation is not sold, the payback rises up to 28 years. Therefore, from the economical point of view, photovoltaic generation power plants with surplus sale are more attractive than self-consumption installations, as the payback period is around 12 years. Consequently, a change in the current Spanish regulations is necessary to support individual self-consumption photovoltaic installations, to make them as profitable as photovoltaic generation power plants.
In this paper, a new algorithm for optimal management of distributed energy resources in facilities with distributed generation, energy storage systems and specific loads – energy hubs – is shown. ...This method consists of an iterative algorithm that manages optimal energy flows to obtain the minimum energy cost based on availability of each resource, prices and expected demand. A simulation tool has been developed to run the algorithm under different scenarios. Eight different scenarios of an energy hub have been simulated to illustrate the operation of this method. These scenarios consist of a demand curve under different conditions related to the existence or absence of renewable energy sources and energy storage systems and different electricity tariffs for grid supply. Partial results in the iterative process of the developed algorithm are shown and the results of these simulations are analysed. Results show a good level of optimisation of energy resources by means of optimal use of renewable energy sources and optimal management of energy storage systems. Moreover, the impact of this optimised management on carbon dioxide emissions is analysed.
•An iterative optimisation algorithm for energy hub resources management is proposed.•A fast and simple method for resources allocation in smart facilities is tested.•A simulation tool has been developed to test strategies in energy hubs.•Savings greater than 50% in energy hubs with distributed resources are simulated.•The algorithm allows taking advantage of tariffs with hourly discrimination.
► Artificial neural network method to predict electrical power load in buildings. ► The method is based on end-uses (EUs) and a time temperature curve forecast model. ► A small number of training ...days similar to the day of prediction is chosen. ► The temperature forecast model is used to select the best training days.
This paper presents the upgrading of a method for predicting short-term building energy consumption that was previously developed by the authors (EUs method). The upgrade uses a time temperature curve (TTC) forecast model. The EUs method involves the use of artificial neural networks (ANNs) for predicting each independent process – end-uses (EUs). End-uses consume energy with a specific behaviour in function of certain external variables. The EUs method obtains the total consumption by the addition of the forecasted end-uses.
The inputs required for this method are the parameters that may affect consumption, such as temperature, type of day, etc. Historical data of the total consumption and the consumption of each end-use are also required.
A model for prediction of the time temperature curve has been developed for the new forecast method (TEUs method). The temperature at each moment of the day is obtained using the prediction of the maximum and minimum daytime temperature. This provides various benefits when selecting the training days and in the training and forecasting phases, thus improving the relationship between expected consumption and temperatures.
The method has been tested and validated with the consumption forecast of the Universitat Politècnica de València for an entire year.
In rural regions with limited access to the power grid, self-reliance for electricity generation is paramount. This study focuses on enhancing the design of stand-alone photovoltaic installations ...(SAPV) to replace conventional fuel generators thanks to the decreasing costs of PV modules and batteries. This study presents a particle swarm optimization (PSO) method for the reliable and cost-effective sizing of SAPV systems. The proposed method considers the variability of PV generation and domestic demand and optimizes the system design to minimize the total cost of ownership while ensuring a high level of reliability. The results show that for the PSO method with 500 iterations, the error is around 2%, and the simulation time is approximately 2.25 s. Moreover, the PSO method allows a much lower number of iterations to be used in the Monte Carlo simulation, with a total of 100 iterations used to obtain the averaged results. The optimization results, encompassing installed power, battery capacity, reliability, and annual costs, reveal the effectiveness of our approach. Notably, our discretized PSO algorithm converges, yielding specific parameters like 9900 W of installed power and a battery configuration of five 3550 Wh units for the case study under consideration. In summary, our work presents an efficient SAPV system design methodology supported by concrete numerical outcomes, considering supply reliability and installation and operational costs.
This paper proposes a method for evaluating the optimal configuration of a hybrid system (biomass power plant and photovoltaic plant), which is connected to the electrical grid, to achieve minimum ...energy costs. The study is applied to a small rural municipality in the Valencian Community, Spain, as an energy community. The approach takes into account the daily energy demand variation and price curves for energy that are either imported or exported to the grid. The optimal configuration is determined by the highest internal rate of return (IRR) over a 12-year period while providing a 20% discount in electricity prices for the energy community. The approach is extrapolated to an annual period using the statistical data of sunny and cloudy days, considering 23.8% of the year as cloudy. The methodology provides a general procedure for hybridising both plants and the grid to meet the energy needs of a small rural population. In the analysed case, an optimal combination of 140 kW of rated power from the biogas generator was found, which is lower than the maximum demand of 366 kW and 80 kW installed power in the photovoltaic plant, resulting in an IRR of 6.13% over 12 years. Sensitivity studies for data variations are also provided.
The World Health Organization (WHO) warns that the presence of magnetic fields due to the circulation of industrial frequency electrical currents may have repercussions on the health of living ...beings. Hence, it is crucially important that we are able to quantify these fields under the normal operating conditions of the facilities, both in their premises and in their surroundings, in order to take the appropriate corrective measures and assure the safety conditions imposed, in force, by regulations. For this purpose, CRMag® software has been developed. Using the simplified Maxwell equations for low frequencies, CRMag® calculates and represents the magnetic flux density (MFD) that electrical currents produce in the environment. Users can easily model electrical facilities through a friendly and simple data entry. MFDs calculated by CRMag® have been validated in real facilities and laboratory tests. With this software, exposure levels can be studied in any hypothetical scenario, even in inaccessible zones. This allows designers to guarantee that legal limits (occupational, general population, or precautionary levels related to epidemiological studies) are fulfilled. A real case study has been described to show how the reconfiguration of conductors in a distribution transformer substation (DTS) allows significant reductions in MFD in some points outside the facility.
In reliability studies of isolated energy supply systems for residential buildings, supply failures due to insufficient generation are generally analysed. Recent studies conclude that this kind of ...analysis makes it possible to optimally design the sizes of the elements of the generation system. However, in isolated communities or rural areas, it is common to find groups of dwellings in which micro-renewable sources, such as photovoltaic (PV) systems, can be installed. In this situation, the generation and storage of several houses can be considered as an interconnected system forming a cooperative microgrid (CoMG). This work analyses the benefits that sharing two autonomous installations can bring to each one, from the point of view of reliability. The method consists of the application of a random sequential Monte Carlo (SMC) simulation to the CoMG to evaluate the impact of a simple cooperative strategy on the reliability of the set. The study considers random failures in the generation systems. The results show that the reliability of the system increases when cooperation is allowed. Additionally, at the design stage, this allows more cost-effective solutions than single sizing with a similar level of reliability.
The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the ...reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.
PDF
