Off-grid Operation Research Articles

Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system

With the application of renewable energy in building energy systems (BES), an increasing number of power grids require building energy systems coupled to realize off-grid operation which is one type of energy flexible and grid responsive operations. In this case, deep reinforcement learning (DRL) algorithms have gained more and more attention in the operation control of BES due to their strong fitting ability and model-free utilization characteristics. However, mainstream DRL algorithms cannot solve the reinforcement learning problem of hybrid action spaces, which also restricts its further application in BES including variety of energy sources. In this paper, we firstly use a multi-agent deep reinforcement learning algorithm (MADRL) to solve the RL problem of hybrid action spaces in the building controls domain. The proposed algorithm is validated on a measured dataset of a real office building in Japan. The results show that compared to the currently used baseline control logic, MADRL can achieve a 60% improvement in off-grid operation tasks. For battery safety, MADRL can reduce unsafe battery runtime by at least 80%. Furthermore, through experiments we find that the single-agent DRL algorithm cannot solve the reinforcement learning problem with hybrid action spaces. The MADRL framework achieves stable training and optimization by layering problems and agents.

Control of Single-Phase Distributed PV-Battery Microgrid for Smooth Mode Transition With Improved Power Quality

In this article, multiple parallel photovoltaic (PV) inverters based microgrid is developed to enhance the reliability and accessibility of electricity in remote areas. A self-adaptive control strategy is presented for a microgrid to ensure a seamless transition between the islanded mode (IAM) and grid-connected mode (GCM) of operation with a proportional power sharing. This microgrid consists of two solar PV inverters based on local distributed generation (DG) units. Each PV-inverter unit feeds an active power to support local loads and the power demand of each local load is shared between respective DG units and microgrid. The PV-battery-interfaced main unit with a self-adaptive control strategy in parallel with a PV array supported unit-2 is considered to operate in the IAM and GCM of operation. The control strategy achieves transient free operation during load disturbances, power variation at the PV array side, and off-grid operation. Moreover, the microgrid ensures high-quality performance at the point of common coupling and provides the seamless transition between GCM and IAM to guarantee a continuous power to critical loads. The microgrid simulation and experimental results are presented under various operating conditions, such as load variation, intermittent power generation, and automated mode transition between GCM and IAM.

Operational optimization for off-grid renewable building energy system using deep reinforcement learning

With the application of renewable energy in single office buildings, an increasing number of power grids require building systems coupled with renewable energy to realize off-grid operation. However, the uncertainty of renewable energy sources and the safety of the corresponding energy storage equipment have become major challenges for these systems. Reinforcement learning has made considerable progress in the field of building control as an advanced control algorithm; however, research on its application to the off-grid operation of renewable energy systems, particularly the specific reward function design is limited.Therefore, this study considered the off-grid operation of a renewable building energy system and the safety (prevention of deterioration) of its battery as optimization goals. This study is based on a real building energy system, through the operation control of generators, solar photovoltaics, and batteries to achieve optimization purposes. Aiming at these two optimization goals, this study introduces a detailed reward function design and complete reinforcement learning workflow through a Gaussian distribution. Two deep reinforcement learning (DRL) algorithms were trained and verified by offline reinforcement learning based on the measured data-sets of actual existing buildings in Japan. The results show that the proposed reinforcement learning design can better achieve the two optimization goals of off-grid operation and battery safety under ordinary and extreme conditions. On off-grid operation tasks, the best DRL algorithm can achieve a mean hourly grid power purchase error of less than 2 kWh for the entire optimization window. Furthermore, the proposed deep reinforcement learning algorithms can simultaneously maintain a maximum average 7.72 h of battery unsafe state over the entire 168-hour optimization window.

An integrated framework for feasibility analysis and optimal management of a neighborhood-scale energy system with rooftop PV and waste-to-energy technologies

Hybrid energy systems with renewable energy penetration have become popular solutions to improve energy security and realize the carbon neural target in urban areas. In this paper, a holistic framework is proposed for the sustainable development of a neighborhood-scale energy system integrating rooftop PV and waste-to-energy technologies. Under this framework, potential assessment of local renewable energy, optimization approach, and sensitivity analysis are integrated for the optimal design and management of the hybrid energy system with uncertain analysis. Besides, the economic, efficiency, and environmental performance under grid-connected and off-grid operation modes are compared. This proposed framework is applied to a neighborhood area in Beijing. The results show that the grid-connected hybrid energy system will still rely on traditional energy supply with low cost but great carbon emissions. In the off-grid mode, 11 % of the rooftop area will be encouraged to deploy PV panels, which satisfying 39 % of the total electricity demand. Moreover, although decreasing the PV capital cost, raising the electricity price, and increasing carbon emission penalty cost will stimulate the development of local renewable energy, compulsive renewable energy penetration policy is more effective to promote the traditional energy system transformation and reduce carbon emissions.

Mission Critical Microgrids: The Case of the Alcântara Space Center

This article reports the conception and design of a mission critical microgrid to serve a critical infrastructure application, namely, the Alcântara Space Launch Center, a government military facility in Brazil. The assumptions, general characteristics, requirements and particularities arising from the application are established and discussed in detail. High resilience and energy security are required in critical periods of operation, demanding a power supply infrastructure composed of redundant and dispachable sources capable of supporting n−1 generation contingencies. Whereas, in the remaining time, economic and environmental aspects take place as main requests. Operation scenarios, on both grid-connected (on-grid) and isolated (off-grid) modes, are formulated as optimization problems and simulations have been performed to analyze these scenarios, which are reported and analyzed here. The off-grid operation, which is clearly more complex, is preferentially addressed, especially in scenarios with contingencies. In this context, the proposed microgrid is conceived to satisfactorily balance requirements such as the economy, pollutant emission reduction, high reliability, resilience and operational security, which are requested by this critical infrastructure application.

Reliability evaluation of an aggregate power conversion unit in the off-grid PV-battery-based DC microgrid from local energy communities under dynamic and transient operation

In a rural/local energy community (LEC), a PV-battery-based DC microgrid (MG) normally operates at the off-grid/islanded mode. At this off-grid operation mode, the reliability of power converters in both the PV system and the battery energy storage system (BESS) could be reduced by dynamic operation scenarios of the DC microgrid which are frequently repeated, such as the intermittency of PV-system output power, the random fluctuation of load power. Indeed, the dynamic operation of the PV-generating system and the load system in the off-grid DC microgrid could lead to a certain decrease in the reliability of DC–DC bidirectional converters of the BESS, boost converters of the PV system, and buck converters for low-voltage DC loads because of withstanding different power flows to provide a source-load power balance in the DC microgrid. Moreover, transient operation scenarios of the off-grid DC microgrid can significantly impact the reliability of the mentioned power converters. To make the above assumptions more clear, reliability analysis of an aggregate power conversion unit (APCU) in the off-grid PV-battery-based DC microgrid under dynamic and transient operation in the local energy community is presented in this paper. The APCU contains the boost converter of the PV system, the bidirectional converter of the BESS, and the buck converter of the low-voltage DC load system. The main objective of the paper is to shed light on calculations of the dynamic-voltage-dependent failure rate (DVDFR) and fault-current-dependent failure rate (FCDFR) of the APCU from dynamic and transient operation conditions respectively in the off-grid LVDC microgrid. Then, a novel combination of the useful-time-dependent failure rate (UTDFR), the DVDFR, and the FCDFR is proposed to evaluate the system-level and component-level reliability of the APCU in the off-grid DC microgrid. Markov-state transition diagrams are applied for the APCU’s reliability assessment. Experimental results show that the reliability of the bidirectional power converter is more affected by the dynamic and transient operation than that of the boost or buck converters in the APCU. In addition, the DVDFR of the APCU is almost smaller than its FCDFR, but the system-level reliability of the APCU could be significantly reduced by dynamic cases in the islanded DC microgrid. Last but not least, the MTTF and MTBF of the APCU could be dramatically decreased by the dynamic and transient operation of the DC microgrid.

AC unbalanced and DC load management in multi-bus residential microgrid integrated with hybrid capacity resources

This paper presents a control scheme including resources and load management in the residential DC microgrid. The DC microgrid is supported by fuel-cell, solar-cell and battery. The DC, AC single-phase and AC three-phase loads with 50 Hz frequency are integrated. The DC microgrid is connected to the external 60 Hz AC three-phase network. An efficient multi-bus topology is proposed for the microgrid and it is formed by various AC/DC buses to supply the loads and managing the resources. The main bus of system is a 470 V DC bus and it is connected to the external 440 V/60 Hz AC grid. The main DC bus supplies three LV, MV and HV DC buses with 100, 220, and 110–380 V, respectively. The HV DC bus produces a variable output DC voltage between 110 and 380 V in order to regulate the load power (i.e., motor speed). The MV DC bus is connected to 220 V/50 Hz AC single-phase loads. The connections between DC microgrid with AC loads and AC external gird are made by single-phase or three-phase inverters. The interface inverters between DC bus and AC loads are operated to control power, torque, speed, frequency and voltage of loads. The unbalanced AC loads are appropriately balanced by proper control of interface inverters. The resources and inverters are efficiently controlled to enable operation of residential building under both off-grid or grid-tied conditions. The coordination of fuel-cell, solar-cell and battery can supply a fixed 8 kW power to external grid and supply the internal loads under all outages and off-grid conditions. The simulations demonstrate that the proposed control realizes all the objectives including AC/DC load management, unbalanced load amendment, frequency adaptation, and off-grid operation.

FogChain: A Blockchain-Based Peer-to-Peer Solar Power Trading System Powered by Fog AI

Microgrids, gaining traction from rising distributed generation for carbon reduction, demand novel solutions to regulate on- and off-grid operations, as well as both energy and monetary transfers between the microgrid and the central grid and among different microgrid participants. This research aims to develop and validate an intelligent microgrid management system to secure the competitiveness of Singapore’s energy market, by leveraging the inherent synergy between two emerging technologies, i.e., blockchain for Peer-to-Peer (P2P) solar power trading and fog computing for grid infrastructure management. For this vision, we have developed FogChain, an integrative, cost-effective, and scalable microgrid operating system (MGOS), consisting of three technical service layers: 1) a novel microgrid information infrastructure based on the fog-computing paradigm (i.e., intelligence on edge); 2) a blockchain-based microgrid service layer, providing smart contract and decentralized control capabilities for grid application development; and 3) a microgrid application layer (i.e., P2P energy trading) over the blockchain-based grid service. This MGOS would fundamentally transform how solar power is traded among participating electricity prosumers, leading to potentially new operational and business models. We have implemented the FogChain system and conducted extensive experiments to verify its performance advantages. Our results demonstrate that FogChain can efficiently process energy auction among 1000 participants with 1.1 s delay on average, reduce transmission cost up to 20% under the loss-aware trading mechanism, and reduce the solar yield prediction error to 0.11. Our system prototype suggests that FogChain provides a promising solution for efficient decentralized energy trading and intelligent distributed control for microgrids.

Reliable IoT-Based Monitoring and Control of Hydroponic Systems

This paper presents the design and implementation of iPONICS: an intelligent, low-cost IoT-based control and monitoring system for hydroponics greenhouses. The system is based on three types of sensor nodes. The main (master) node is responsible for controlling the pump, monitoring the quality of the water in the greenhouse and aggregating and transmitting the data from the slave nodes. Environment sensing slave nodes monitor the ambient conditions in the greenhouse and transmit the data to the main node. Security nodes monitor activity (movement in the area). The system monitors water quality and greenhouse temperature and humidity, ensuring that crops grow under optimal conditions according to hydroponics guidelines. Remote monitoring for the greenhouse keepers is facilitated by monitoring these parameters via connecting to a website. An innovative fuzzy inference engine determines the plant irrigation duration. The system is optimized for low power consumption in order to facilitate off-grid operation. Preliminary reliability analysis indicates that the system can tolerate various transient faults without requiring intervention.

Improving the Power Outage Resilience of Buildings with Solar PV through the Use of Battery Systems and EV Energy Storage

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h.

Techno-economic analysis of both on-grid and off-grid hybrid energy system with sensitivity analysis for an educational institution

The Renewable Energy Sources (RES) have marked a new trend in the Indian energy market and sustainable energy development. In countries like India, the consumers are more welcomed to be prosumers by utilizing the primary renewable energy sources such as solar, wind, biogas, geothermal etc. The renewable energy generation is highly dynamic and thus, Hybrid Energy Systems (HES) strengthens its scope in self-dependent energy generation of the consumers. This paper proposes techno-economic analysis of a Hybrid Energy System (HES) design that has been set up at Kamaraj College of Engineering and Technology (KCET), Virudhunagar, Madurai, Tamil Nadu, India. The paper investigates the on-grid and off-grid operation of the proposed HES. The system is designed and optimized by using HOMER (Hybrid Optimization Model for Electric Renewables) software. The feasibility of the HES system is studied under different dispatch strategies such as Cycle Charging (CC) and Load Following (LF) for both on-grid and off-grid operation. Further, the sensitivity analysis is carried out for the proposed HES system with the fuel price and scaled annual solar radiation as sensitivity variables. The Net Present Cost (NPC) and Cost of Energy (COE) of the system under different architectures are determined and it is found that, the on-grid HES with LF dispatch is the optimal system design for the selected location. The performance of the system is studied in both technical and economic perspectives to optimally design the HES components and a minimum NPC of $ 7.66 M has been obtained.

Compressed Air Energy Storage Experimental Platform with off-grid Operation

The compressed air energy storage(CAES) experimental platform can play a role in research and training. Due to the large changes in experimental working conditions and many sudden abnormal situations, it is easy to disturb the safe operation of the power grid when it is integrated into the power grid. By constructing a three-layer compressed air energy storage experimental platform, equipped with a complete test and operation monitoring system, using multiple sets of high-power light bulbs as loads, the experimental platform can be operated off-grid. It has the advantages of simple structure, low cost, wide adaptable voltage range, easy observation of experimental results, etc., and has broad application value.

Grid-connected Micro-grid Power Supply and Distribution Transformer Capacity Optimization System Design in the Electricity Sales Environment

With the current gradual opening of our country’s electricity sales market and the successive improvement of related policies for grid-connected micro-grids, grid-connected micro-grid power supplies and their distribution transformer capacity have received attention and development. Based on the good development of my country’s electricity sales market environment, this paper designs the grid-connected microgrid power supply optimization system and its distribution and conversion capabilities to optimize the quality of electricity sales and optimize the grid configuration. In view of the fact that the operation of the microgrid system in my country’s market environment is more flexible at this stage, after a long time simulation test, a simulation analysis of the operation status of the grid-connected microgrid is carried out. In the course of the experiment, the electricity price in the distribution point process and the real-time changes of the electricity purchase price in the market will be considered comprehensively, and the processing in the micro-electricity system based on its distribution transformer capacity optimization system and the load forecast result will be considered. This article uses a power grid project actually constructed in East China as an example to analyze the results of simulation calculations. Then the NSGA-II algorithm is used to solve the optimized model for the cost and benefit composition of the grid. The optimized configuration method of the optical storage microgrid proposed in this paper can ensure the reliable power supply of users’ important loads during the off-grid operation of the microgrid, increase the installed capacity of distributed photovoltaics, and the configured energy storage system also allows enterprises to achieve low electricity prices. Purchase electric energy at any time and use it to supplement the load’s demand for electric energy during peak electricity price periods, thereby increasing revenue. The experimental research results show that the design of the grid-connected microgrid power supply capacity optimization system under the electricity sales environment studied in this paper can effectively reduce the additional investment required by external loads and the capacity of optical storage and distribution transformers. Only then can the economic marketing profit of the microgrid be improved to a certain extent.

Single-phase control of three-phase fuelcell-battery under unbalanced conditions considering off-grid and grid-tied states

This paper presents an advanced control strategy for balancing the time-varying and unbalanced loads by using the fuel cell and battery under the grid-tied and off-grid operations. In the grid-tied operation, the fuel cell and the battery are coordinately controlled to balance the three-phase unbalanced active and reactive powers of the loads. In this case, the received three-phase active-reactive powers from the grid become completely balanced by the given strategy. In the off-grid, the diesel generator supplies the active-reactive powers of load and the unbalances are handled by coordinated operation of fuel cell and battery. As well, the voltage of off-grid system is improved by injecting adequate reactive power to the system through the fuel cell, battery and diesel generator. The dynamic stability of the system is evaluated under non-linear disturbances like grid outage and single-phase fault. The time-varying unbalanced active-reactive powers are balanced under both off-grid and grid-tied states by implementing the proposed model. The purpose of the proposed control system is to balance the unbalanced load from point of view of the upstream grid at all time periods. In order to realize such function in the model, the load is modeled by two terms including the balanced and unbalanced parts. The unbalanced share is supplied by the inverters (DC bus) and the balanced term of load is supplied by the grid. The designed system is resilient under the events like grid outage and short circuits. As well, the system properly regulates and controls the load variations and unbalances.

Power quality multi-step predictions with the gradually increasing selected input parameters using machine-learning and regression

Autonomous off-grid systems dependent upon Renewable Energy (RE) sources are characterized by stochastic supplies of the fluctuating low short-circuit power. Power Quality (PQ) standards define load characteristics in electric power systems and their ability to function properly without failures. Monitoring, prediction and optimization of PQ parameters are necessary to maintain their alterations steady within the prescribed range, which allow fault-tolerant operation of various electrical devices. It is not possible to measure complete PQ data for all possible combinations of dozens of grid-connected appliances, whose load specifics and collisions primarily determine the course of PQ parameters and their eventual disturbances. Self-adapting PQ prediction models based on Artificial Intelligence (AI) are required as induced power is influenced particularly by changeable weather conditions in real off-grid operation mode of systems using RE. A novel multi-step PQ prediction algorithm is proposed, which develops AI models with the gradually increasing number of selected input PQ-parameters. In each next step a more complex model is formed, using an additional co-related PQ-input to calculate its target PQ-output with a better accuracy. PQ-models with the progressively growing PQ-inputs, using their data predicted in the previous step, can better approximate and estimate the target quantity. The presented results show this training and feature selection procedure can step by step improve accuracy of PQ-models for unknown combinations of off-grid connected household appliances.

Analytical Design of Coreless Axial-Flux Permanent Magnet Machine With Planar Coils

This paper presents the design of an axial-flux permanent magnet (AFPM) generator with two rotors and one coreless stator with planar coils. This geometry has reduced the total mass of the generator and production demands of the machine. The geometry of the coreless AFPM machine enables direct analytical calculation of the magnetic field in the air gap. The analyzed geometry is reduced to the equivalent 2D model. The equivalent circuit parameters are evaluated for the proposed design of the planar coil by the calculation of the magnetic field produced by permanent magnets and stator winding separately. These principles of analytical design of the AFPM machine are integrated into the developed software “deSAX”. The presented analytical approach and the results are validated by 3D finite-element analysis (FEA). The generator is then produced and tested under the off-grid operation and the maximal power 200 W (750 rpm) is obtained according to calculated output characteristics.

SPWM Smoothing Control Method Based on Wind Power Grid Connection

Energy is the foundation of economic development and technological development. Facing the present situation of non-renewable energy decline, wind power generation has been developed rapidly. However, the problem of unstable output voltage of wind power generation due to unstable wind speed needs to be solved, and traditional solutions cannot make the generated electric energy meet the national standards for grid-connected and off-grid operation. In this paper, the electric energy generated by wind turbine is rectified by bridge rectifier circuit and using large capacity capacitor filtering to generate DC with flat waveform. Then, using SPWM inversion technology, the normal rotation wave with the same frequency as the power grid is used as the modulation wave and according to the required voltage amplitude, and the triangle wave with appropriate duty ratio is calculated by SPWM smoothing control theory as carrier wave. Subsequently, accurate filtering is carried out and grid-connected and off-grid operation is carried out through the self-aligning device. Finally, with the help of MATLAB simulation platform, the wind turbine is simulated to work under different wind conditions, and whether the generated electric energy meets the grid-connected and off-grid operation standards is judged, which further determines the reliability and authenticity of the theory.

Technical feasibility evaluation of a solar PV based off-grid domestic energy system with battery and hydrogen energy storage in northern climates

Self-sustaining off-grid energy systems may require both short-term and seasonal energy storage for year-around operation, especially in northern climates where the intermittency in both solar irradiation and energy consumption throughout the year is extreme. This paper examines the technical feasibility of an off-grid energy system with short-term battery storage and seasonal hydrogen storage, comprising a water electrolyzer and a fuel cell. The study is based on data from a currently grid-connected residential single-family house in Finland with an existing 21 kWp photovoltaic (PV) installation and a ground source heat pump based heating system. Energy system performance is simulated using real PV power generation data as well as data on grid electricity import and export from the house over a three-year period to find the minimum combination of battery and hydrogen storage system capacities capable of year-round off-grid operation. It is concluded that technically feasible solutions are available for the proposed setup. The most significant factor affecting the system dimensioning is found to be high peak electric power demand during times of low PV power generation, indicating a need for smart power control. Demand for battery storage capacity is found to be significant only to about 20 kWh. Fuel cell and electrolyzer nominal powers of at least 4 kW and 5 kW to 7 kW, respectively, were found to be sufficient for off-grid operation with the studied system. The storage capacity demand for 170–190 kg of hydrogen annually is, however, impractically large for residential houses.

The viability of solar photovoltaic powered off-grid Zero Energy Buildings based on a container home

With strongly decreasing prices of photovoltaics (PV) and battery storage in the past decade, together with incentives for modular construction in China, shipping containers have been suggested as suitable building modules. Transportable modules will ease the process of returning the modules to the factory for reuse and recycling of structural steel at the end of their lifetime. A previous comparison between the proposed case studies has shown that the off-grid case can reduce the global warming potential by 76% in Shanghai when compared to a business-as-usual scenario. Generally, there is only a limited number of studies of container buildings with a simulation of the annual energy need in the literature. Particularly there is a lack of studies of residential buildings designed for off-grid operation. This study expands a previous study of the case buildings by improved control strategies in TRNSYS to reduce the required PV capacity and thereby the embodied emissions. For instance, thermal storage has been included to reduce the amount of dumped energy. Also, ventilation and indoor temperature have been controlled based on ambient temperature, state of charge, day, and time. In cold climates, Photovoltaic thermal (PVT), floor heating, and a generator have been added to increase the heating load. The results show that providing thermal comfort in the heating season is the main challenge, both because of the lower coefficient of performance for the heat pump and due to the lower PV generation. According to the predicted effects of climate change, the energy need for space heating is predicted to be reduced by on average 23% towards 2050 for the five climate zones in China which are considered, which would make it easier to provide thermal comfort. The current study helps to provide a reference to lower the costs for planning and improve the quality. The study is significant because China, who account for about 50% of new construction globally, is targeting to increase its prefabricated construction by 30% within ten years.

Resilience oriented vehicle-to-home operation based on battery swapping mechanism

This paper considers battery swapping and energy resilience in the building integrated with electric vehicle (EV), reserve battery, and solar energy. The building is able to continue the operation under both the off-grid and grid-tied states. The EV and reserve battery are coordinated for battery swapping strategy, energy cost reduction, and energy resilience improvement. The battery swapping strategy enables the EV to leave the building at each hour of the day when needed by the owner. Such flexible battery swapping model makes the EV available for owner at all time periods of the day even when the EV has just arrived to the building with empty battery. This strategy increases the acceptance rate of EVs and deals with the concerns regarding lengthy charging times. The coordinated EV and reserve battery also enables the system to supply the load demand following events like outage of solar or grid energy. Such model significantly improves the energy resilience in the given home energy management system. The solar power uncertainty is modeled by scenarios of performance and the problem is solved under set of uncertainty scenarios. The super capacitor is applied to remove the solar uncertainties under off-grid operation. The results including various cases verify that the enhanced battery swapping strategy is able to improve the resilience and reduce the energy cost.