Hybrid simulation and optimization-based capacity planner for integrated photovoltaic generation with storage units

The paper presents a wind–photovoltaic-thermal hybrid-driven two-stage humidification and dehumidification desalination system for remote island regions lacking access to electricity and freshwater resources. By conducting an analysis of the wind and solar energy resources at the experimental site, a suitable wind power station and photovoltaic power station are constructed. The performance of the wind–solar complementary power generation system is then evaluated based on factors such as output power, seawater desalination load power, battery compensation output, system energy consumption, and water production costs. A variable step gradient disturbance method based on the power–duty ratio is proposed for tracking the maximum power point (MPPT) of wind power generation. The output power of the photovoltaic power generation system is optimized, employing a fuzzy logic control (FLC) method to track the MPPT of photovoltaic power generation. This approach effectively addresses the issues of slow speed and low accuracy encountered by traditional MPPT algorithms in tracking the maximum power point (MPP) of both photovoltaic and wind power generations. In order to ensure that the desalination system can operate stably under different weather conditions, eight working modes are designed, and a programmable logic controller (PLC) is used to control the system, which provides a guarantee for stable water production. Experimental results demonstrate that the system exhibits stable performance, achieving a maximum water output of 80.63 Kg/h and daily water yield is 751.32 Kg, the cost of desalination equipment is 1.4892 USD/t.

Including learning and forgetting processes in Agent-Based simulation models: Application to police intervention in out-of-hospital cardiac arrests

Although the problem of a lack of an adequate denominator of crime rates has been widely recognized, relatively few solutions have been found. This paper proposes a sophisticated method of analyzing crime hot spots using global positioning system (GPS) tracking data and agent-based simulation modeling. The goal of the proposed analysis technique is to estimate a population at risk across time and space at the micro-level, such as streets and locations (e.g. parks and playgrounds). In order to create valid estimates of a population at risk and to illustrate how the estimated population at risk can be used in crime analysis, this paper presents a series of three sub-studies. First, GPS tracking data were obtained for two weeks for 80 children in the second and fifth grades from one school located in a suburb of Tokyo, Japan. GIS software was used to analyze the GPS data in order to identify streets and places that the children frequented. Second, agent-based simulation modeling was developed to extend the proposed analysis method to school districts that had not participated in the GPS sub-study. In the process of developing a simulation algorithm, the validity of simulation modeling was checked by comparing the simulation results with the GPS tracking data. Third, victimization surveys with maps were conducted in six schools (including the school that participated in the GPS sub-study) that identified locations of crime incidents. Victimization survey results were overlaid with the analysis results of GPS data and agent-based simulation modeling in order to examine crime hot spots. The proposed technique of agent-based modeling produces an accurate denominator of crime rates at the micro-level which can then be used to analyze crime hot spots properly. In particular, this paper discusses how crime analysts can identify different types of hot spots, such as crime generators and crime attractors, by examining the number of crime incidents and the magnitude of crime rate. This paper argues intervention strategies need to be tailored for each type of crime hot spots, as these crime hot spots develop through different mechanisms. Finally, this paper discusses limitations of the proposed technique and the direction of future research.

Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization – ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs. While tumor images provide spatial information, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.

Generally, solar PV Panel is constructed at fix tilted angle at different locations, such as: street light poles, rooftops, residential buildings, and canopy. The energy production from solar PV generation system is vary and influenced by site (location) and weather parameters. Solar PV generation analyzed in this research is Dual Axis Sun Tracker (DAST) Solar PV Generation. Dual axis sun tracker is a system controller to ensure solar PV panel following the sun path. Furthermore, energy production from DAST-based solar PV generation were measured and collected using energy meter as well as by using automatic weather station for the weather parameters. From these measrument data, it can be correlated the influence of weather parameters to the energy production from DAST PV generation system. The correlation between weather parameters and energy production are analyzed by statistical analysis using SPSS software and multiple linear regression models. From the results and analysis, it can be shown the amount of energy production from DAST-based solar PV Generation are influenced by weather parameters, namely: temperature, humidity, ultraviolet radiation, and solar radiation with significant level up to 98.2%.

In this paper, the solar energy resources in Guangdong Province are divided into three categories according to the development and utilization value, and the development and evaluation of distributed photovoltaic projects and grid-connected photovoltaic projects are carried out for each region. According to the calculation results and sensitivity analysis, the development and evaluation suggestions for photovoltaic power generation projects in Guangdong Province are obtain.

The continuous development of photovoltaic and energy storage systems has enabled the emergence of microgrids, which can operate with alternating or direct current (DC), depending on the components characteristics. Since photovoltaic panels and battery storage systems operate in DC, connecting them via a DC bus simplifies power conversion, especially for DC loads. A key challenge in microgrids is defining an effective control strategy, which may be centralized, decentralized, or distributed. In this sense, the main proposal of this paper consists in the development of a decentralized control strategy for a stand-alone DC microgrid with photovoltaic generation and battery energy storage, using a controlled voltage droop, based on virtual impedance. This paper aims to contribute with the description of the implementation steps of the local controllers, using classical control techniques based on transfer function modelling. In addition, the assembling steps of a low-cost and small-scale DC microgrid prototype are presented. The control approach maintains DC bus voltage within an appropriate range without requiring communication among system components. Moreover, it supports modular expansion by enabling the seamless integration of new generation or storage units. To validate the strategy, a microgrid prototype was developed using a 60~W PV panel, a 12~V, 7~Ah lead-acid battery, and the BOOSTXL-3PhGaNInv evaluation module with a F28379D digital signal controller for converter implementation. Experimental results, including the battery charging process and variations of solar irradiance and load demand, demonstrate the effectiveness and robustness of the proposed control system.

One of the current technologies widely used to extract the earth’s renewable energy is solar modules, which harness energy from the sun; however, their operating conditions and their energy storage capacities vary greatly under different weather conditions. The high-speed Flywheel Energy Storage System (FESS) presents a promising prospective solution for solar energy storage and utilization. Accurately modeling FESS can lead to its efficient design and control. In this paper, a fully integrated FESS, with comprehensive photovoltaic (PV) system, has been modeled to simulate the overall energy harvesting capabilities, power output, and efficiency. The solar module has been modeled using the one-diode solar cell model technique to predict the overall system efficiency and determine the most efficient time of day for switching between the FESS’s charging (morning time) and discharging mode (night time). To validate the developed model using experimental data, four 250 Wp Ankara Solar PV modules and a Balance of System (BOS) were installed. The solar irradiance, wind speed, cell temperature, solar module, FESS output voltage and current were logged during the system’s operation. The theoretical model predicted that the energy output for the test day was 4.80 kWh, while the experimental analysis showed that the solar modules produced 4.68 kWh, only 2.5% percentage difference. The theoretical and experimental power curves followed the same trends throughout the day, which assures that the model could accurately predict the daily energy output of the solar array. The efficiency of the solar module was determined to be 15.3%. The solar module simulation serves as a repeatable replication of the actual solar module source, which enables convenient, low-cost estimation of the solar module-FESS system under different environmental conditions. The developed solar module was integrated with a brushless DC motor and flywheel models to simulate the FESS response. Relative to the input solar energy input of 4.68 kWh, the daily energy stored in a flywheel was 3.51 kWh, giving the overall solar-module-FESS system an efficiency of 74.7%. After the experimental setup completion of FESS, the integrated solar module-FESS model will be tested for its overall power output and efficiency against the traditional solar module-battery-system.

Based on the modelling steps discussed in Part I, this chapter aims to present ways in which agent-based simulation models of supply chains can be developed and used to improve the performance of these systems in both normal and abnormal situations. An industrial supply chain with a network of several independent companies is a good example of a socio-technical system. The physical and social networks of the actors involved in their operation collectively form an interconnected, complex system in which the actors determine the development and operation of the physical network and, likewise, the physical network affects the behaviour of the actors. In this type of system, the many interactions taking place in the social and physical subsystems can result in the complex, dynamic behaviour of the supply chain as a whole. Accordingly, any attempt to improve the functioning of the supply chain requires a comprehensive understanding of this behaviour under different supply network configurations. Most of the current approaches to the modelling and simulation of supply chains do not capture the rich socio-technical dynamics present. The agent-based modelling approach, however, seems to be very promising as a means to address this complex behaviour. To demonstrate its applicability, we will present agent-based simulation models for two different industrial supply chains: an oil refinery and a multi-plant chemical enterprise. Using the models described in this chapter, the outcomes of the system under a broad range of possible agent behavioural rules and environmental events can be explored, and improved levels of system functioning can be identified.

This paper discusses some issues concerning the idea of taking equation-based simulation models (EBM) as formal, mathematical specifications of agent-based models (ABM) for Social Simulation. The paper tries to identify some of the benefits that such point of view could bring to the development of ABM, as well as some of the preliminary problems that an ABM developer has to face, in order to guarantee the credibility of his model. The paper first identifies the benefit of the EBM support for the formal verification of the correction of the ABM, given the correction of the EBM. Then, it identifies a preliminary set of crucial implementation details that are not covered by EBM specifications, and that the simulation developer has to define to make the ABM operational, under the constraint of keeping the ABM model compatible with the EBM. In particular, the two issues of the local vs. global scope of the agents' perceptions and of the degree of personalization of the agents are identified as crucial ones. Some preliminary concepts are proposed to help to solve those two problems.

Actual energy consumption in buildings is typically different from predictions during the design phase. While differences in occupant energy usage characteristics play an important role in this variation, actual energy estimation software do not account for this factor. This paper proposes a new approach for energy estimation in buildings using a combination of traditional energy calculation software along with agent-based simulation modeling. First, the difference in energy consumption levels for different types of occupancy energy usage characteristics is identified by building energy models adapted for each type of behavior. Then, an agent-based simulation model simulates the influence that people with different behaviors have on each other, resulting in potential changes in energy usage characteristics over time. By combining these two methods, more customized energy studies can be performed resulting in more accurate energy consumption estimates.

Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution

This paper focuses on the integration of a photovoltaic generator (GPV), a PEM fuel cell (PEMFC), an electrolyzer and a hydrogen storage tank for sustained power generation. In the proposed PV-FC hybrid system, the PV generator feeds a variable load through a DC-DC converter controlled by an MPPT control (maximum power point tracking) and transfers energy to the electrolyzer side if possible to produce hydrogen which will be stored in a tank for a later use in the fuel cell (PEMFC). When the PV system cannot completely meet the load demands, the FC system provides the lack of power. The MATLAB-Simulink environment is used for the simulation of the PV-FC hybrid proposed system. Simulation results are presented in order to highlight the performances of proposed hybrid system.

Distributed energy resources DER constitute a relatively new paradigm in the electric power industry. The concept is gaining popularity because of the inherent modularity of DERs. They can be regarded as more flexible power sources, albeit with limited capacity, than centralized power units. DERs are power generation or storage units that are connected directly to the distribution network or connected to the network on the customer side of the meter. These energy sources can include microturbines, fuel cells, wind power, solar power, and several forms of energy storage. If barriers can be removed, DERs can be valuable resources to the power generation and distribution industry. They have the potential to improve reliability, power quality, and environmental pollution while deferring transmission and distribution TD • Storage aspects of DER; • Microgrids; • Economic and operational aspects of DER; • Impact on power quality and reliability; and • Interconnection standards. Although different circles define distributed generation DG in somewhat different perspectives, the most widely accepted version defines it as being a generation source located in proximity to load centers and having a generating capacity of anywhere from 10 kW to 50 megawatts. Often, such generating sources use nonconventional fuels, and many of the units are designed to produce both electricity and heat, in applications simply known as combined heat and power CHP . This represents a departure from the current industry practice of locating very large fossil-fired generating sources at remote locations, closer to the fuel source and transmitting the generated power over high-voltage transmission lines to load centers. Many of the DER technologies, such as those that depend on solar power, wind power, and biomass, are inherently renewable in nature. Since solar and wind power outputs depend on an intermittent resource, predicting the amount of power that will be available at certain times of the day often becomes difficult. With large amounts of intermittent generation, operating the renewable DER within an existing electric utility’s system can bring about

Solar power plays an important role in the transition to a low-carbon energy system, especially in the context of China's latest carbon peaking and neutralization policy. Since the efficiency of Photovoltaic (PV) generation is fundamentally determined by weather conditions, it is characterized by uncertainty, intermittency and constant fluctuation. All these characteristics would pose significant threats to the operational security and stability of the power system. Therefore, it is essential to investigate the optimized operation of distribution network considering the integration of PV generation. For this sake, this paper proposes a novel PV generation modelling framework by applying the concept of “clearness index”, which can effectively capture the output characteristics of PV generation in the short term. Based on this model, this paper establishes an optimization model of distribution network operation, which takes PV plants into consideration. Through a series of case studies tested on the REDS-32 system. It is found that in order to ensure the economic operation of DN, PV power has to be curtailed under certain conditions.