УПРАВЛІННЯ ПІДКЛЮЧЕНОЮ ДО МЕРЕЖІ ФОТОЕЛЕКТРИЧНОЮ СИСТЕМОЮ ДЛЯ ПОТРЕБ ОБ’ЄКТУ З МОЖЛИВІСТЮ ПІДВИЩЕННЯ ПОТУЖНОСТІ ПОНАД ЛІМІТ НА СПОЖИВАННЯ

Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial-temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.

In fossil fuel-driven electricity markets, gas power plants are mostly the price setting technology in peak demand hours. At the same time, gas technology has the role of balancing out the volatility of increasing shares of intermittent renewable generation. This interdependence, coupled with recent fears of supply shortages, raises the question how gas-fired generation can be replaced with carbon-free technologies. Controlling for fundamental supply and demand in the interconnected markets, we quantify the effect of intermittent renewable and nuclear generation in neighboring markets on the use of natural gas for electricity generation in Germany. Findings indicate that cross-border intermittent wind, PV, and nuclear generation significantly reduce the need for German gas. We further show Germany’s dependency on French cross-border nuclear energy to supplement the gas unavailability during the gas crisis period. We argue for larger market coupling and flexible generation alternatives and adaptive demand solutions including cross-border capacities for substituting gas when renewable generation is low across Europe.

The current energy crisis raised concern about the lack of electricity during the wintertime, especially that consumption should be cut at peak consumption hours. For the building owners, this is visible as rising electricity prices. Availability of near real-time data on energy performance is opening new opportunities to optimize energy flexibility capabilities of buildings. This paper presents a reinforcement learning (RL)-based method to control the heating for minimizing the heating electricity cost and shifting the electricity usage away from peak demand hours. Simulations are carried out with electrically heated single-family houses. The results indicate that with RL, in the case of varying electricity prices, it is possible to save money and keep the indoor thermal comfort at an appropriate level.

Real-Time Pricing in the Nordic Power markets

Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated to adopt energy management and optimization techniques, where accurate forecasting of PV generation is essential. While the existing literature provides valuable insights, a comprehensive review of methodologies specifically tailored for the forecast of PV generation in buildings remains scarce. This study aims to address this gap by analyzing the forecasting methods, data requirements, and performance metrics employed, with the primary objective of providing an in-depth review of previous research. The findings highlight the critical role of improving PV energy generation forecasting accuracy in enhancing energy management and optimization for individual buildings. Additionally, the study identifies key challenges and opportunities for future research, such as the limited exploration of localized environmental and operational factors (such as partial shading, dust, and dirt); insufficient data on building-specific PV output patterns; and the need to account for variability in PV generation. By clarifying the current state of PV energy forecasting methodologies, this research lays essential groundwork for future advancements in the field.

As electric vehicles (EVs) have being rapidly deployed into the transportation industry, they can be used for a reduction in peak load of electric power distribution systems. Therefore, the objective of this paper is to present a typical method that analyzes the effect of charging and discharging EVs on (a) a reduction in peak load, (b) an increase in under- (e.g., 0.95 PU) and over-voltage, (e.g., 1.05 PU), and (c) intermittency in voltage and total power generation. For this purpose, this paper (a) proposes a simple strategy that charges EVs at night and discharges them to the grid at peak demand hours, (b) defines a load factor and a change in total power generation from an interval to another, and (c) models a distribution system that hosts charging stations for EVs and photovoltaic (PV) systems. Then, to calculate the power flow of such a distribution system, including PV and charging systems, in steady state, this paper uses the Open Distribution Simulator (OpenDSS), a power system analysis computer program useful for calculating the power flow of electric power systems. Finally, it analyzes the effect of charging and discharging EVs on a distribution system.

In this paper, a practical methodological framework is proposed for the optimal planning of an isolated rural microgrid considering distributed energy resources (DERs) such as photovoltaic systems (PV), run-of-river micro-hydro (MH) systems, battery energy storage systems (BESS) to supply energy to isolated rural microgrids. The methodology considers the formulation of a stochastic optimization problem (SOP) that requires a previous evaluation of the renewable energy sources available in the area and the demand load profile of the community. In addition, to estimate the daily demand load curve of the rural community, the household classifier based on a Self-Organizing Map (SOM) method is used, that provides demand patterns by the use of the socio-economic characteristics of the community obtained in a survey. The methodology is applied in a community located on the banks of the Aguarico river. Three scenarios are evaluated, in which scenario I and II, only renewable energy sources are considered, scenario II is modelled including a budget constraint, that is not included in scenario I, and scenario III incorporates the implementation of gas generator set (GGS) unit in order to obtain a technical and economical viable solution, based on the renewable energy sources assessment. The results showed that a microgrid based only on renewable energy sources available in the community was not feasible because it leads to prohibitive costs. Therefore, a GGS unit was required to supply energy during the peak demand hours in the months where the hydro sources are not available, demonstrating the effectiveness of the proposed methodology.KeywordsDistributed energy resourcesIsolated rural microgridsLoad profile generatorOptimal planningStochastic optimization

Our company has been selling the peak-cut & shift type ice storage packaged air conditioner on sale since 1994. Since this system can largely reduce the power consumption without running the compressor during the power peak demand hours, the system is the most suitable for the buildings, factories where the demand control is of concern.The system features are the following : 1. Power consumption cut ratio of 90% or more during the peak demand hoursAt the peak hours, the compressor is stopped, the operation mode is switched to the refrigerant pump mode of the low power consumption (one tenth of that of the compressor power), and the cooling is carried out only by the thermal radiation of the ice accumulated by the electric power at nighttime.2. 20% cut power consumption except during the peak demand hoursThough the compressor is running, the thermal radiation of the ice accumulated by the electric power during nighttime increases the supercool in order to achieve the economical operation. Thus. even the outdoor unit equivalent to the 10-horsepower class can achieve the capacity of the 13-horsepower class, and this can cut the power consumption by 20%.3. The ice packing factor is as high as 70%, and the ice storage unit is compact.In the general use office building, the power consumption is compared between the ordinary package type air conditioner and the ice storage type air conditioner, The test is carried out on two systems of the 10-horsepower class at the same floor : The power consumption and its utility cost of May thru October are compared. As the result, the utility cost of the ice storage system is about two thirds of that of the standard multipurpose building type, total savings is about 166, 000 yen. As shown by the result, the peak-cut & shift type package air conditioner of our company will be the most attractive method to smoothen the electric load. As a manufacturer, our company will try to improve the performance and reduce the cost.

A survey-based electricity demand profiling method for developing countries: The case of urban households in Bangladesh

In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO2 emissions. This article aims to explore the feasibility of integrating photovoltaic systems (utilizing vacuum collectors) and combined utilities (system heat and electricity) in a hybrid setup, leveraging existing technical infrastructure with necessary modifications. A key aspect is to perform calculations on the amount of heat and electricity generated from these systems. The study analyzes the demand for heat and electricity among consumers compared to the estimated production from renewable sources. Calculations also include the potential energy savings and CO2 emission reductions achievable through the proposed solutions. The findings indicate that hybrid photovoltaic systems with heat storage could effectively address energy issues in urban housing estates, given adequate support and community involvement. The innovative methodology employed in this study encompasses both analytical and experimental research approaches. The analysis employs advanced statistical techniques and data integration to enhance understanding of the phenomena studied, while the experimental research provides robust results through controlled variable manipulation and precise measurement tools, thereby verifying the study’s objectives.

Due to the shortage of electric power in isolated rural areas of Ecuador, implementing a photovoltaic power generation system is an optimal, viable, and sustainable alternative that can reduce the gap in electric power coverage in the nation. However, since the geographical location of a possible PV system implementation directly affects its optimal performance, it is essential to propose generation forecasting algorithms that use actual data from the place under study. For this reason, this paper compares Holt-Winters and Seasonal Variation forecasting methods applied to two rural locations in Ecuador. These methods use historical records of meteorological conditions such as solar irradiation and ambient temperature to achieve a short-term forecast of photovoltaic generation. Percentage errors obtained in the forecast power and the Pearson correlation coefficient are calculated as performance measures. The results show that although both algorithms have similar characteristics when getting the photovoltaic generation prediction, the Holt-Winters method presents improved behavior.

04/02795 Control-oriented nonlinear modelling of moltencarbonate fuel cells: Shen, C. et al. International Journal of Energy Research, 2004, 28, (5), 403–410

ABSTRACTThe interest in energy storage systems is increasing, since it provides an excellent solution to store the low-cost excess energy from the energy sources, which are available at peak demand hours. This paper presents a new compressed-air storage system that combines ambient air and hydraulic oil, in order to store energy in compressed-air form and benefit from the advantages of both pneumatic and hydraulic systems. The process consists of charging and discharging cycles, however, this paper investigates the discharging cycle, where a new technique of Small-Scale Compressed Air Energy Storage (SS-CAES) system is realised. The new idea in RC-CAHES is to obtain higher efficiency in energy conversion machines during charging and discharging processes with numerous advantages over conventional types of energy storage systems. This study demonstrates the effectiveness of this technique by proving that it has higher efficiency than a similar Compressed Air Energy Storage (CAES) systems.

A demand response based solution for LMP management in power markets

In India one of every three rural households depends on kerosene based lighting systems that are characterized by low illumination and hazardous smoke. Given the existing and foreseeable gap between demand and supply, universal modern energy access can only be achieved in the short to medium term through a combination of grid and off-grid approaches. The paper presents a case study of solar photovoltaic based micro-grids (generating 75 W–1 kW) used to meet basic lighting requirements of 30 rural communities during peak demand hours (6–10 pm). The communities have adopted micro-level solar-based off-grid systems, which are owned and managed by local entrepreneurs with a 45% equity stake. Case study analysis of this project, in the state of Uttar Pradesh in India shows that: 1. Grid-connected (and even unconnected) rural communities without assured lighting offer a latent market for decentralized alternatives. 2. Access to finance networks at the local level facilitates private investments in off-grid solutions. 3. Technology innovation and customization is necessary for customer satisfaction and management efficiency. 4. Affordable off-grid solutions based on solar energy can be an economically viable and socially acceptable alternative to fossil fuel systems. Analysis of the project output concludes that decentralized solutions and private investments can play a major role not only in providing universal modern energy access but also in enhancing livelihood options at the village level.