An effective secured peer-to-peer energy market based on blockchain architecture for the interconnected microgrid and smart grid

Methods of participating power spot market bidding and settlement for renewable energy systems

Digitization in the mining industry and machine learning applications have improved the production by showing insights in different components. Energy consumption is one of the key components to improve the industry’s performance in a smart way that requires a very low investment. This study represents a new hardware, software, and data processing infrastructure for open-pit mines to overcome the energy 4.0 transition and digital transformation. The main goal of this infrastructure is adding an artificial intelligence layer to energy use in an experimental open-pit mine and giving insights on energy consumption and electrical grid quality. The achievement of these goals will ease the decision-making stage for maintenance and energy managers according to ISO 50001 standards. In order to minimize the energy consumption, which impact directly the profit and the efficiency of the industry, a design of a monitoring and peak load forecasting system was proposed and tested on the experimental open-pit mine of Benguerir. The main challenges of the application were the monitoring of typical loads machines per stage, feeding the supervisors by real time energy data on the same process SCADA view, parallel integrating hardware solutions to the same process control system, proposing a fast forest quantile regression algorithm to predict the energy demand response based on the data of different historical scenarios, finding correlations between the KPIs of energy consumption, mine production process and giving global insights on the electrical grid quality.

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Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

https://www.mdpi.com/1424-8220/21/9/3157/pdf

Cluster Analysis and Model Comparison Using Smart Meter Data.

An interdisciplinary research perspective on the future of multi-vector energy networks

Realistic energy commitments in peer-to-peer transactive market with risk adjusted prosumer welfare maximization

Regarding the modern power smart grid, distribution consumers and prosumers are highly concerned about power quality (PQ). In fact, they would prefer to pay higher prices for a reliable and good quality power supply. Unfortunately, utility operators still aim for reliability alone, ignoring the quality of supply voltage and current. There are no clear guidelines for monitoring, penalizing, or implementing PQ-based tariff schemes in LV distribution systems. In addition, the implementation of a PQ-based tariff requires a real-time measuring mechanism at the user end, which is very expensive and difficult to understand for a domestic consumer. This paper presents a novel, low-cost, efficient, and user-friendly smart PQ meter to overcome these issues and limitations. It is essentially a PQ analyzer with energy metering functionality, which implements a novel PQ-based tariff scheme that penalizes consumers violating the PQ limits and provides incentives for a good PQ profile. It measures as many as 28 parameters and keeps track of the PQ for both the consumer and the grid in real-time. This paper demonstrates the specifications, design, and testing of the meter and proves the validity of the concept by practical implementation. The meter is practical, feasible, and economical for implementing PQ-based tariff schemes in LV distribution systems or smart grids.

Development of a Smart Meter for Power Quality-Based Tariff Implementation in a Smart Grid

The distributed generation (DG) planning with the varying pattern of the practical load is difficult as it calls for the frequent changes in DG size and system configuration, which is neither feasi...

Mini Shaji Thomas

Papers

Realistic energy commitments in peer-to-peer transactive market with risk adjusted prosumer welfare maximization

Development of a Smart Meter for Power Quality-Based Tariff Implementation in a Smart Grid

A Coordinated Framework of DG Allocation and Operating Strategy in Distribution System for Configuration Management under Varying Loading Patterns