This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough. 

The energy sector is experiencing a revolution that is fuelled by a multitude of factors. Among them are the aging grid system, the need for cleaner energy and the increasing demands on energy sector. The demand-response program is an advanced feature in smart grid that strives to match suppliers to their demands using price-based and incentive programs. The objective of the work is to analyse the performance of the load shedding technique using dynamic pricing algorithm. The system was designed using multi-agent system (MAS) for a DC microgrid capable of real-time monitoring and controlling of power using price-based demand-response program. As a proof of concept, the system was implemented using intelligent physical agents, Java Agent Development Framework (JADE), and agent simulation platform (REPAST) with two residential houses (non-critical loads) and one hospital (critical load). The architecture has been implemented using embedded devices, relays, and sensors to control the operations of load shedding and energy trading in residential areas that have no access to electricity. The measured results show that the system can shed the load with the latency of less than 600 ms, and energy cost saving with an individual houses by 80% of the total cost with 2USD per day. The outcome of the studies demonstrates the effectiveness of the proposed multi-agent approach for real-time operation of a microgrid and the implementation of demand-response program.

Recently, the traditional power grid has been evolving into a new type of intelligent system known as smart grid. The new smart grid uses information and communication technologies to automate and optimize the power generation and distribution process. Despite improvements, there are still challenges that cannot be solved using existing technologies. Blockchain is an emerging technology with unique features to solve some of remaining challenges in smart grids. In this paper, we review the recent developments in this area. We describe the existing smart grid challenges, explore blockchain features suitable for smart grids, and highlight notable existing successful implementations. The review shows several applications such as peer to peer energy trading and autonomous asset management that have been enabled by blockchain technology. It also highlights challenges such as performance and regulations that might hinder the future use of blockchains in smart grids.

Demand Side Management, DSM, is a program supported by the smart-grid which aims at matching the energy consumption and production. Several techniques for demand-side management have been proposed, including load-shedding, time of use pricing, real-time pricing, and critical peak pricing. In this work, we propose a distributed load-shedding algorithm using the multi-agent system. The agents in residential areas collaborate to reduce the energy demands using various forecasting techniques. The computational distributed framework is provided via fog computing to minimize power consumption, costs, and latency when designed using LoRaWAN protocol.

The depletion of water table coupled with the decrease in rainfall due to climate change has pushed international communities to look for better ways to save water. Furthermore, access to the water table in disconnected communities in off-grid locations is a challenge to many African countries. These challenges negatively affect farming activities where irrigation plays an important role. In this paper, we propose a sustainable and smart DC microgrid irrigation system using multi-Agent systems along with Internet of Things technologies. The system is composed of a microgrid connected water pumping system and Internet of Things enabled sensors for irrigation. An algorithm that uses an agent-based approach to regulate energy demand from the PV system and controls irrigation is also introduced. The features of the proposed system have been compared against features for related systems.

This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough.

The iGrid project deals with the design and implementation of a solar-powered smart microgrid to supply electric power to small rural communities. In this paper, we discuss the roadmap of the iGrid project, which forms by merging the roadmaps of KIC (knowledge and Innovation Community) and CDE (Challenge-Driven Education). We introduce and explain a five-gear chain as Challenge, Education, Research, Innovation, and Deployment, called CERID, to reach the main goals of this project. We investigate the full chain in the iGrid project, which is established between KTH Royal Institute of Technology (Sweden) and University of Dar es Salam (Tanzania). We introduce the key stakeholders and explain how CERID goals can be accomplished in higher educations and through scientific research. Challenges are discussed, some innovative ideas are introduced and deployment solutions are recommended.

To improve the living standard of urban communities and to render the healthcare services sustainable and efficient, e-health system is experiencing a paradigm shift. Patients with cognitive discrepancies can be monitored and observed through the analyses of power consumption of home appliances. This paper surveys recent trends in home-based e-health services using metered energy consumption data. It also analyses and summarizes the constant impedance, constant current and constant power (ZIP) approaches for load modelling. The analysis briefly recaptures both non-intrusive and intrusive techniques. The work reports an architecture using IoT technologies for the design of a smart-meter, and fog-computing paradigm for raw processing of energy dataset. Finally, the paper describes the implementation platform based on GirdLAB-D simulation to construct accurate models of household appliances and test the machine-learning algorithm for the detection of abnormal behaviour.

The increasing interest in integrating renewable energies source has raised concerns about control operations. The presence of new energy sources, distributed storage, power electronic devices and communication links make a power system’s control and monitoring more complex and adaptive than ever before. Recently, the use of agent-based distributed control has seen to have a significant impact on the grid and microgrid controls. The load-shedding technique is among the features used to balance the power consumption in the power system upon less power production. Towards achieving these, different mechanisms, algorithms, challenges, and approaches have been developed and hence need to be reviewed and integrated from the system solution perspective. This research focuses on the review of the state-of-the-art load-shedding techniques, whereby the focus is on control algorithms, simulation platforms and integrations, and control devices for DC microgrid. The research also investigates open issues and challenges that need further investigations. The analyses reported in the paper upholds the importance of the distributed multi-agent system, MAS, in implementing distinct control operations including load-shedding. The effectiveness of the control operations using MAS rely on low-latency and secure communication links in which IoT has been branded as a promising technology for implementing distributed MAS.

Smart-grid is a complex system that incorporates distributed control, communication, optimization, and management functions in addition to the legacy functions such as generation, storage, and control. The design and test of new smart-grid algorithms require an efficient simulator. Agent based simulation platforms are the most popular tools that work well in the control and monitoring functionalities of the power electric network such as the microgrid. Most existing simulation tools necessitate either simulated or static data. In this paper, we propose a hardware-in-loop simulator for dc-microgrid. The simulator reads the power generated by the PV panels and the battery SoC using Raspberry PI. A physical agent that MRS on Raspberry PI sends the real-time data to a dc-microgrid simulator that runs on a PC. As a proof of concept, we implemented a load-shedding algorithm using the proposed system.