Energy management with the support of dynamic pricing strategies in real micro-grid scenarios

The future of energy and technology management is being shaped by innovations, data-driven insights, and the development of smart solutions that aim to address global energy challenges while promoting sustainability. As the demand for energy continues to rise, there is a growing need for advanced technologies that optimize energy production, distribution, and consumption. Key innovations, such as renewable energy technologies, smart grids, and energy storage systems, are paving the way for more efficient and sustainable energy management. The integration of data analytics and Internet of Things (IoT) devices allows for real-time monitoring and predictive analytics, enabling better decision-making in energy systems. These technologies are driving the shift towards decentralized energy models, where consumers can generate, store, and manage their energy consumption autonomously. Data-driven insights are crucial in optimizing energy usage and enhancing system reliability. Machine learning and artificial intelligence (AI) are being utilized to predict energy demands, identify inefficiencies, and optimize operational processes in real-time. By leveraging big data, energy managers can gain a deeper understanding of consumption patterns, enabling the creation of tailored energy solutions that reduce waste and lower costs. Furthermore, the development of smart cities and smart homes is transforming how energy is consumed, with interconnected systems that adjust energy use based on real-time conditions. As energy management becomes more sophisticated, the role of technology in developing smart solutions for energy efficiency and sustainability will continue to expand. The convergence of AI, IoT, and renewable energy will play a critical role in building a resilient and low-carbon energy infrastructure. The future of energy and technology management is not only about meeting the growing energy demand but also about achieving environmental sustainability and operational efficiency. Embracing these innovations will be key to unlocking the full potential of energy systems in the years ahead.

As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

Demand-side management (DSM) has a key role in the Smart Grid (SG) concept to control the demand-side consumption and reduce peak loads. The ability to shift peak loads and provide the energy efficiency through better demand-side management is currently one of the most promising approaches to solve problems related to peak demand. DSM has some different terms such as demand-side energy management (DSEM), load energy management (LEM), demand response (DR), and automated load management (ALM) and all terms refer to the balancing of energy generation and consumption. DSM includes all the process in demand energy systems such as utilities renovation operations, metering, energy pricing, monitoring, customer comfort, home energy management systems, and so on. In addition, DSM has superior advantage that it is less expensive to intelligently influence a load than to build a new power plant or install some electric storage device. All these DSM strategies are used for optimum and efficient energy consumption. In this chapter, the general perspective is given for DSM under SG concept and describes the DSM architecture and its benefits of the customer side and utility side. Also, it explains the DR techniques and classified DR programs and give some information about dynamic pricing and smart metering. Then the impact of DR programs is discussed in residential energy management perspectives. It also gives some details about home energy management (HEM) concept. Finally, it discusses about DSM standards. In conclusion, the existing DSM applications and what could be done in the future works are discussed.

Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

Smart homes or the homes of the future will be equipped with advanced technologies for user comfort and entertainment. Intelligent systems will be available to ensure this comfort and reliability. With these technological advancements comes further energy management. The concept of domestic energy efficiency is a concern at present and will be, in the future. So how do we optimize homes and users as to how they conserve energy? Domestic user's energy usage represents a large amount of total electricity demand. Typical home energy systems utilize a rudimentary form of energy efficiency and management. In this paper we look at a Demand Response and Demand side management system model to curb this situation. The demand response system is achieved by the utility turning on/off smart power plugs wirelessly throughout the home based on peak and off peak periods via communication through its smart grid. To help consumers shift their loads during these times, appliance power sources that can act autonomously based on wired or wireless signals received from the utility via its smart grid is required. Users in response to this, connect their appliances to these plugs by generating their own hierarchy system by prioritizing their appliance usage. Whereas the demand side management system allows users to manually configure dates and times for the turning on/off of the smart power plugs wirelessly through the user's smart user interface. Therefore, an energy efficient future smart home that can save the user on monthly expenditure and save on energy simultaneously.

Despite the benefits of demand response in energy management, the non-existence of its key concepts; dynamic pricing and smart grid, in some countries makes its impracticable in these countries, therefore making energy management unattainable for their consumers. This paper proposed a Smart Distribution Board (SDB) using a priority model for energy management in non-smart grid network. An historical consumption signatures of user’s loads were used to develop a priority model for load units of the SDB. Performance comparison was carried out between the SDB and a conventional Distribution Board which has no level of intelligence. Results obtained indicated that the SDB correctly emulated the energy usage pattern of users, thereby ensuring load preference is maximally satisfied autonomously within a limited budgeted energy and period.

Energy management has enhanced sustainability, dependability, and efficiency in smart grids. Urbanisation, technology, and consumer behaviour have boosted need for innovative power use and price control systems. The paper intends to construct ML for smart grid power use and price prediction. This work used an advanced shark smell-tuned flexible support vector machine (ASS-FSVM) to forecast smart grid price and power use. Weather stations, smart meters, and market price databases document power use and pricing. The quality and consistency of data are enhanced via the processes of cleaning and normalizing inputs. PCA reduces dimensionality by extracting pre-processed data characteristics. Optimized and tested FSVM models can anticipate smart grid power use and pricing. ASS may identify the most important dataset properties. The research evaluates electricity consumption forecasting using accuracy (98.05%), recall (98.93%), precision (97.10%), and F1-score (98.04%), and electricity price predicting using MAPE (4.32%), RMSE (5.80%), MSE (8.50%), and MAE (2.95%). The recommended strategy greatly increases forecast accuracy, helping utilities improve grid stability, demand responsiveness, and customer pricing.

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques.

Energy management for the industrial sector in smart grid system

The advent of the Internet of Things (IoT) has ushered in transformative changes across diverse sectors, notably the energy domain, spawning the innovative concept of smart grids. This research delves into the development and deployment of an IoT-based prototype smart grid system, aiming to augment energy efficiency, reliability, and management. The system integrates current and voltage sensors, coupled with an ESP32 microcontroller, enabling real-time monitoring, control, and optimization of the prototype electrical grid. Leveraging Google Firebase as a cloud service for storing real-time data (current, voltage, and power), the prototype includes an architectural model simulating industrial, commercial, and residential areas within a city. The model features illumination controlled by three output relays linked to the ESP32 via a 2N222 transistor. A grid control interface, developed with JavaScript and React, interfaces with the Firebase real-time server to manage relay states. This interface empowers a distribution company to remotely designate powered sections, mimicking scenarios like sectional maintenance or compulsory load shedding. The collaborative effort in mini-grid design underscores the efficiency gains achieved through IoT implementation in conventional electrical grid systems, emphasizing time and labor savings in energy management.

IoT-based monitoring and control of substations and smart grids with renewables and electric vehicles integration

The discrete-time second-best dynamic road pricing scheme

Closed loop elastic demand control by dynamic energy pricing in smart grids

Manufacturers are scrambling for relief from today's energy expenses and price volatility. Most industry decision-makers believe the solution is to seek the lowest available energy prices. Too often, managers fail to grasp the opportunities offered by energy management, which focuses on both consumption and prices. Industry can be resistant to energy management for a variety of reasons. Simply put, energy management has no traditional place in the typical manufacturer's chart of organization, job descriptions, and performance accountabilities. While technology is fundamental to energy efficiency, it is people who make it work in an organizational context. DuPont, Frito-Lay, Unilever, and Kimberly-Clark are a few of the forward-thinking companies that have found ways to build energy management into their daily operations to positive effect. The Alliance to Save Energy (ASE) is documenting these companies' experiences in a series of case studies that refl ect the organizational and behavioral aspects of corporate-wide energy management. Case studies show that energy management motives and approaches are somewhat varied—there is no "one size fits all" solution. ASE offers a typology of industrial energy management strategies to illustrate the range of opportunities available to industry. Ultimately, it is a manufacturer's organizational character that determines its ability to manage energy consumption. A checklist included in this article allows the reader to diagnose a manufacturer's aptitude for undertaking various energy management strategies.