A tool for the design of desalination plants powered by renewable energies

The grid connection of large-scale wind power and photovoltaics has changed the frequency and voltage characteristics of the traditional power grid. The renewable energy generation system with new energy as the main body has put forward higher requirements for the performance of new energy grid connection, including the multi-energy complementary system(MECS) of renewable energy and energy storage. There is no unified measurement standard for the performance of access to the grid. Therefore, starting from the performance of MECS connected to the power grid, such as autonomous frequency regulation, autonomous voltage regulation, active/reactive power control, etc., a performance evaluation method of MECS connected to the power grid is given. Referring to the relevant standards for renewable energy and energy storage grid connection, a three-level index system for evaluating the grid-connected operation performance of MECS including target-level, performance-level and detail-level is constructed. The fuzzy evaluation method is used to analyze the grid-connected performance of a multi-energy complementary power station in Qinghai Province. The validity and applicability of the proposed evaluation method are verified.

Dynamic energy management for photovoltaic power system including hybrid energy storage in smart grid applications

Australia has a vast land mass characterised by more than 35,000km of shore-line and an abundance of renewable sources (e.g., solar and wind energy). Despite the existence of much potential to utilise sustainable pathways of power generation, there remains a general reliance on electricity generated from larger plants which are mostly grid-connected but fossil-fuel operated. In Western Australia, only a fraction of its coastal areas and inland mass is serviced by the South West Interconnected (grid) System. For the majority of its regional communities, decentralised power generation forms the prime source of power provision. This exacerbates the situation with regard to accessing electricity due to the elevated cost of obtaining fuel (for power generation) as well as reliance on smaller, less-efficient, generator sets. For many small (remote) or coastal communities' access to potable water is limited alongside good availability of renewable energies. This provides opportunity for utilising renewably powered stand-alone energy systems to help deliver the power needed to directly run utilities, operate desalination systems and reduce the associated emissions footprint. This investigation uses modelling to analyse the performance of small-scale stand-alone (energy) systems incorporating Reverse Osmosis (RO), providing up to 15litres/day potable water. Through inclusion of physical models representing different hardware components in a Solar-Photovoltaic (PV) system, this research provides an insight into the interaction between the availability of solar energy, energy conversion into DC electric power via PV panels, power conditioning (DC to AC), battery charging/discharging and the power needed for desalination. This paper not only highlights a modelling methodology for such systems but also demonstrates how individual (system) components may be characterised and seasonal variations (of solar irradiance, localised wind speed and ambient temperature) included in the simulations. Simulations undertaken include consideration for unit quantities (solar irradiance per square metre and temporal resolution of predicted irradiance to 1hour). Such approaches provide a basis for future studies into energy system scalability, energy efficiency in small-scale (stand-alone, renewably powered) desalination systems as well as the deployment of other (non-battery) energy storage media to increase renewable energy utilisation. Modelling yields solar irradiance predictions which are compared to measured data at locations typical of Perth, West Australia. The models also accommodate considerations for the effects of localised wind speed and ambient temperature on predicted PV panel performance. This yields more accurate conversion characteristics of PV and helps provide better resolved (dynamic) renewable energy (input) data for the simulations. Laboratory based experiments are used to verify the efficiency, water recovery ratio and power characteristics of RO as well as other energy system components. Simulations undertaken using MATLAB help analyse the energy system over a yearly period. Results allow predictions of total (renewable) energy availability, excess (renewable) energy (not captured due to battery capacity) and total potable water production (under two different amounts of daily water demand). Outcomes are discussed with regard to the benefits of incorporating more advanced predictive modelling methodologies and alternate means of (battery- free) energy storage for stand-alone PV energy systems.

The constraints in conventional energy forecasting models in the USA are increasingly being appreciated as the energy landscape evolves. Such models are generally constructed on linear assumptions that do not capture energy generation and consumption patterns as dynamic and multi-dimensional. This research project aimed to develop effective machine-learning models to interpret historical trends in energy generation and capacity in the United States. With access to massive datasets for various energy sources—fuel-based to renewable and nuclear energy—this research endeavors to discover actionable information that can optimize energy output and improve grid stability. The dataset used is a comprehensive overview of energy production in America, integrating information from a variety of sources, ranging from fossil fuels to renewables and nuclear energy. This extensive dataset is sourced from reliable sources such as the U.S. Energy Information Administration (EIA) which supplies vast historical and current data on energy consumption and production patterns; the Federal Energy Regulatory Commission (FERC) which supplies information on regulatory frameworks and market forces that affect energy production; and smart grid management systems that deliver real-time data on energy flows and grid performance. Selecting appropriate machine learning models is crucial to process engineered features and derive actionable insights effectively. For this purpose, we use Logistic Regression, Random Forest Classifier, and the Support Vector Machines. The model performance table indicates how well three machine learning algorithms—Logistic Regression, Random Forest, and SVM—classify between renewable and non-renewable energy sources. All three models are highly accurate, with SVM having a slight edge, followed by Random Forest and lastly Logistic Regression. AI-powered energy forecasting is revolutionizing energy infrastructure planning in America through the provision of sophisticated information to guide investment in grid upgrades and expansion. As America transitions to dependence on renewable energy, AI-based forecasting is essential to optimize the integration of solar and wind energy production onto the grid. The variability of these renewable resources poses special challenges for grid managers, who need to balance demand and supply in real time. Moreover, AI can be used to identify gaps in renewable energy capacity and storage. The infusion of AI-based insights into the energy sector has far-reaching implications for U.S. policymakers, equipping them with fact-based tools to efficiently apply energy reforms. With evolving energy patterns, legislators need to adjust regulations to facilitate a seamless transition to a more sustainable energy system.

This chapter proposes the challenges faced by the present renewable energy scenario and contribution of power electronics and optimization techniques. India is one of the leading energy harvester countries in the world. Power electronics is to keep power system operation stable, to harvest electric power from renewable energy sources (RESs), and to reduce energy consumption. This chapter has been largely focused on power electronics for wind energy, photovoltaic energy, and energy storage systems, pointing out some aspects related to configuration for integration, energy storage technologies, reliability, and grid connection. Apart from this, modern optimization techniques for MPPT control using artificial intelligence like fuzzy control and neural network control are also presented in this chapter.

As the goal of "achieving carbon peak by 2030 and achieving carbon neutrality by 2060" was proposed, the development of new energy plays a vital role in the power system of our country. At present, China’s new energy development is dominated by wind power and solar energy. However, new energy has the characteristics of high volatility and instability, which bring great challenges to new energy grid connection. How to deal with the problems caused by new energy consumption and grid connection is the direction of urgent research. Based on the current development of the electricity market, this paper builds a cooperative alliance of complementary thermal power and new energy on the supply side, so that thermal power can reasonably lower its output in the peak period of new energy output and reasonably increase its output in the trough period. In this way, promoting the consumption of new energy and the safety of grid connection and maximizing market benefits can be achieved.

Energy storage is a critical component to supply local energy generation for both grid and off‐grid connected facilities and communities, enabling localized grid independent energy secure power in cases of emergencies or unreliable traditional grid use. The high cost and energy security of importing fuel to islanded grids has led to a growing need to generate power onsite with alternative and renewable energy technologies while reducing facility costs of importing electrical power. However, utility grid operators are being faced with the challenges of intermittent and variability in energy production from renewables. Therefore, energy storage is crucial to balance micro and utility grids, improve efficiency, reduce fuel consumption, and provide critical power in the event of power outages. There has been particular interest in reversible solid oxide fuel cells (RSOFCs) in the energy sector for electricity, energy storage, grid stabilization and improvement to power plant system efficiency due to favorable thermodynamic efficiencies of high temperature steam electrolysis. Boeing has been active in the development of a fully integrated, grid tied RSOFC system for micro grid and commercial utility energy storage using Sunfire fuel cell technology. In this system, excess grid energy or curtailed power generated by renewables is sent to the system operating in electrolysis mode to produce H2. The H2 is stored and then used in the system's fuel cell mode to provide supplemental power to the grid during peak hours or as needed. As part of this program, Boeing has developed a H2 storage and compression system, power distribution system, and master controller to interface with RSOFC subsystems. Sunfire developed a reversible solid oxide cell module with a power output of 50 kW in SOFC mode and 120 kW input in electrolysis mode producing 3.5 kg H2 hr−1. The system was demonstrated while connected to the local utility grid and operated in a microgrid test environment. This paper will discuss the development, integration, and demonstration of the RSOFC system.

1 - Techno-economic study of off-grid renewable energy systems in Pindar and Saryu Valleys, Uttarakhand, India

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...]

Chapter 9 - Energy storage integration within interconnected micro energy grids

One-quarter of the world’s population lives without access to electricity. Unfortunately, the generation technology most commonly employed to advance rural electrification, diesel generation, carries considerable commercial and ecological risks. One approach used to address both the cost and pollution of diesel generation is renewable energy (RE) integration. However, to successfully integrate RE, both the stochastic nature of the RE resource and the operating characteristics of diesel generation require careful consideration. Typically, diesel generation is configured to run heavily loaded, achieving peak efficiencies within 70–80% of rated capacity. Diesel generation is also commonly sized to peak demand. These characteristics serve to constrain the possible RE penetration. While energy storage can relieve the constraint, this adds cost and complexity to the system. This paper identifies an alternative approach, redefining the low load capability of diesel generation. Low load diesel (LLD) allows a diesel engine to operate across its full capacity in support of improved RE utilization. LLD uses existing diesel assets, resulting in a reduced-cost, low-complexity substitute. This paper presents an economic analysis of LLD, with results compared to conventional energy storage applications. The results identify a novel pathway for consumers to transition from low to medium levels of RE penetration, without additional cost or system complexity.

The global transition to renewable energy is imperative to address climate change, energy security, and environmental sustainability. This research paper explores the latest advancements in renewable energy technologies, including solar, wind, and biomass, and proposes a novel hybrid system architecture to overcome the challenges of intermittency, storage, and grid integration. The proposed system integrates multiple renewable energy sources with advanced energy storage solutions, such as lithium-ion and flow batteries, and leverages smart grid technologies for real-time energy management. By employing predictive analytics, machine learning, and demand response strategies, the system optimizes energy production, storage efficiency, and grid stability. Performance evaluation demonstrates significant improvements in energy output, storage efficiency (85%), and grid reliability compared to existing systems.

With the development of energy industry, the power generation and transportation industry have developed from traditional petrochemical energy to renewable energy (RE). Many wind farms and photovoltaic (PV) power plants have been set up. More and more electric vehicles (EV) and EV charging stations are connected to the power grid which has brought new challenges to the safe and stable operation of the power grid. The EV grid-connected charging is accompanied by various faults. The unstable RE also induced lots of new problems. In this paper an intelligent test platform was designed to realize the grid-connected RE station and EV charging station online diagnose and fault prediction. IoT, big data analysis technologies were used to process the collected various characteristic dimensions of multi-heterogeneous data. Based on Random Forest (RF) algorithm machine learning technology was used to realize the online diagnose and fault prediction.

With the upgrading of the “carbon peak and neutrality” policy to a national strategy, high penetration renewable energy grid connection becomes a necessary path for power development. In order to improve the safety, stability and economy of distribution network operation, this paper introduces a price-based demand response model and proposes an optimal configuration method for wind power, photovoltaic system, diesel generator, and battery storage grid-connected microgrid based on real-time electricity price. At the source side, the number of diesel units and energy storage units is calculated with the lowest comprehensive cost of distributed power planning as the objective function under the premise that wind turbines and photovoltaic units are determined. At the load side, according to the demand-price elasticity matrix model, the minimum absolute value of the difference between the power of the electric load and the power generated by renewable energy at 24 moments a day is used as the objective function to calculate the real-time electricity price and derive the electric load after demand response. After alternate iterations of both source and load, the optimal configuration of the microgrid is obtained. The results of the simulation study on a regional microgrid show that the optimization model can effectively reduce the configuration capacity of diesel engines and storage batteries, increase the penetration rate of renewable energy, and increase the economic and environmental benefits of the microgrid while ensuring the reliability of power supply and enhancing the interests of users.

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids