Demand-side management in Grid-Connected Battery System using Neural Network A novel energy management system to improve the efficiency of grid-connected energy storage systems using a deep neural network is developed. The high penetration of renewable energy and decentralization of the grid has led to an increase in the instability of the grid. To reduce this instability, a balance between the consumption demand and production rate needs to be maintained. For this objective, electric vehicle batteries can be integrated with demand-side management techniques using a deep neural network. The controller can be programmed with the timing of the peak and the off-peak hours obtained from the demand curve data and state of charge of the battery. The controller will take two inputs: The time of the day and the State of Charge of the battery. The NN controller will detect the arrival of the peak and will send a message to the EV battery to supply a programmed percentage of power to the household appliances. The direct communication between the grid and the battery can be eliminated to reduce the infrastructure requirements and data processing. The grid can operate successfully during normal working hours and can supply the total power consumption by the loads at any time of the day. The peak to average power ratio can be reduced by operating the EV battery during peak hours for providing that programmed percentage to the appliances for better grid operation. This drained battery will be further fully charging during low loading of the grid and keep ready for the following days’ operation. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption costs.

In PURA’s technical meeting, Bob Keane presented modeling showing heat pump adoption can sharply increase winter peak demand, capital needs and marginal costs; he recommended demand-side measures, separate residential load profiles, and caution on subsidies that could shift costs to non‑heat‑pump customers.

Wind und Sonne versorgen und kostengünstig mit Energie dank Windräder und PV-Anlagen. Doch nachts scheint die Sonne nicht und wenn in lauer Sommernacht kein Wind weht, geht dann mein Kühlschrank aus? ...

With the hot season well underway, residents fear worsening cuts to come, while rising fuel prices and an increased reliance on gas generators threaten to send business costs and commodity prices soaring.

The Electricity Distributors Association (EDA) presented awards to 14 individuals and organizations on March 23, celebrating outstanding leadership, innovation, and service within Ontario’s utility sector. Approximately 650 industry professionals attended the awards ceremony. Brian Wilkie received the Chair’s Citation Award in recognition of his 28 years as CEO of Niagara Peninsula Energy Inc. and seven

Demand-side management in Grid-Connected Battery System using Neural Network A novel energy management system to improve the efficiency of grid-connected energy storage systems using a deep neural network is developed. The high penetration of renewable energy and decentralization of the grid has led to an increase in the instability of the grid. To reduce this instability, a balance between the consumption demand and production rate needs to be maintained. For this objective, electric vehicle batteries can be integrated with demand-side management techniques using a deep neural network. The controller can be programmed with the timing of the peak and the off-peak hours obtained from the demand curve data and state of charge of the battery. The controller will take two inputs: The time of the day and the State of Charge of the battery. The NN controller will detect the arrival of the peak and will send a message to the EV battery to supply a programmed percentage of power to the household appliances. The direct communication between the grid and the battery can be eliminated to reduce the infrastructure requirements and data processing. The grid can operate successfully during normal working hours and can supply the total power consumption by the loads at any time of the day. The peak to average power ratio can be reduced by operating the EV battery during peak hours for providing that programmed percentage to the appliances for better grid operation. This drained battery will be further fully charging during low loading of the grid and keep ready for the following days’ operation. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption costs.

Demand-side management in Grid-Connected Energy Storage System using Deep Neural Network============================================================A novel e...

Demand-side management in Grid-Connected Energy Storage System using Deep Neural Network============================================================A novel e...

Image courtesy Kraken Technologies MAINGAU Energie and Kraken Technologies have extended their partnership to introduce Kraken’s flexible demand management system for energy customers across Germany. ...

… ** … Demand-Side-Management (DSM) Demand-Side-Management (DSM) umfasst eine Reihe von Strategien und Programmen, die darauf abzielen, den Stromverbrauch der Verbraucher zu beeinflussen. Das Ziel ist die Verbesserung der Netzstabilität und die Reduktion von Lastspitzen im Stromnetz. Im Gegensatz zu traditionellen Ansätzen, die sich auf die Erhöhung der Stromerzeugungskapazität konzentrieren (Supply-Side Management), fokussiert DSM auf die Optimierung der Stromnachfrage. Dies kann durch verschiedene Maßnahmen erreicht werden: Lastverschiebung: Der Verbrauch wird zeitlich verschoben, um Lastspitzen zu vermeiden.