DSM scheduling mechanism with pricing schemes using integer linear programming

Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation.

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day-ahead pricing strategy. Based on the Rubinstein–Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak-to-average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.

We present an innovative protocol combining in-network data aggregation and smart meter billing for a smart grid scenario. The former enables an energy supplier to allocate and balance resources. The latter provides dynamic pricing schemes according to fine-grained consumption profiles. Moreover, smart meters and their energy supplier can prove their billing values. Since the energy supplier knows the amount of generated electricity and the consolidated consumption in a round of measurements, the energy supplier can detect energy loss and fraud. To preserve customers' privacy, we use a homomorphic commitment scheme with a homomorphic encryption scheme. All data sent from a meter to any other component in the communication network is either a commitment or an encrypted message. To provide security and privacy, we only require software modifications, leaving the hardware of the smart grid unchanged.

In light of the increasing cost of electricity and the Global Warming campaigns to reduce general electricity usage, there is a growing interest in analysing power consumption in households[6]. By analyzing the electricity usage of appliances more accurate conclusions can be drawn on their efficiency and need for replacement . Furthermore , this can also determine whether an appliance is drawing unusually high amounts of power when turned off and whether it should rather be unplugged. In this way electricity consumption and cost can be reduced[1] . The smart meter project is one of the most important needs these days, In Smart Grid ; the smart meters are versatile role with intelligent capabilities in order to meet the consumer's demands and each objective[5]. Smart meter can measure and communicate detailed real-time electricity usage, facilitate remote real-time monitoring and control power consumptions and consumers are providedwith real time pricing and analysed usage information, which is a technical data to be transmitted to the grid, who are utility providers. More detailed feedback on each appliance to the user[5]. Future smart grids will likely to be more tightly integrated with the cyber infrastructure for sensing, control , scheduling , dispatch, billing and cyber attacks can be detected by using smart meters and also ordering the power demand through online[6]. Every home uses electricity with different consumption and different amounts of watts according to the different types and numbers of the appliances they have[1]. Our proposed system offers a real tine feedback on the entire electricity usage and the consumption of many appliance at device level as a result, This allow the user to monitor measure and compare their energy consumption and electricity level on house hold and on device level[5].

Although privacy concerns in smart metering have been widely studied, relatively little attention has been given to privacy in bi-directional communication between consumers and service providers. Full bi-directional communication is necessary for incentive-based demand response (DR) protocols, such as demand bidding, in which consumers bid to reduce their energy consumption. However, this can reveal private information about consumers. Existing proposals for privacy-enhancing protocols do not support bi-directional communication. To address this challenge, we present a privacy-enhancing communication architecture that incorporates all three major information flows (network monitoring, billing and bi-directional DR) using a combination of spatial and temporal aggregation and differential privacy. The key element of our architecture is the Trustworthy Remote Entity (TRE), a node that is singularly trusted by mutually distrusting entities. The TRE differs from a trusted third party in that it uses Trusted Computing approaches and techniques to provide a technical foundation for its trustworthiness. A automated formal analysis of our communication architecture shows that it achieves its security and privacy objectives with respect to a previously-defined adversary model. This is therefore the first application of privacy-enhancing techniques to bi-directional smart grid communication between mutually distrusting agents.

The state of the art of old-age grids into smart grids provides residents the opportunity to schedule their appliances to consume the energy optimally that leads to potentially balance the demand side as well as the supply side more effectively and minimizes the power Peak-to-Average Ratio (PAR), which ultimately provides benefit to the residents in the form of reduction cost and expense. The Energy Management System (EMS) in the home receives the market and system signals and controls the loads, Heating, Ventilation and Air Conditioning systems (HVAC), storages and local generation units according to the user preferences. This survey encompasses novel home energy management techniques including different shift able and non-shift able load scheduling methods and peak shaving strategies. Several Pricing strategies have been suggested for smart grid such as, Real-Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP), etc. Moreover, this paper discusses the HEM architecture and reveals that the different energy management techniques intelligently schedule the appliances in order to satisfy the maximum resident's comfort level and consume the energy optimally.

Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods sequentially and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator offers the data price simultaneously. We analytically demonstrate that the proposed dynamic pricing scheme can help the operator gain greater revenue and users achieve higher total utilities than those of the baseline static pricing scheme. We construct the social graph using Erdös-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

Growth of population and the inception of new devices every day comes with an incessant rise in energy consumption and has brought great challenges in terms of energy management at the consumer side. With the evolution of technology, smart meters (SMs) are not only considered merely as tools to measure energy consumption but act as a main resource of energy management systems. The application of SM spans over a wide range of advantages, including accurate billing data, information of utilization at the user end, the establishment of two-way communication and remote control of the user equipment. SM is the most essential element of a smart power grid that with the help of any smart energy management system (SEMS), assesses, measures, controls, implements and communicates power allocation, utilization, and consumption at both, single device, and network level. Data provided by the SMs is used by power supply companies to revolutionize power distribution and consumption through various techniques such as, non-intrusive load monitoring and demand-side management (DSM). For efficient data gathering and utilization, internet of things (IoT) is emerging as a key partner in the power industry leading to effective resource management. This paper provides presentation, deployment, and validation of an IoT based SEMS strategy and its related benefits to overcome challenges of energy management at consumer side. The presented SEMS incorporates various communication interfaces and protocols to integrate with any software-based smart solution. In this work, Entrack software is used for data gathering and analysis. As a proof of concept, the presented system is tested and implemented in 4 different buildings of a well-known private company in Pakistan, i.e., Stylo Pvt. Ltd. The case study analysis in this work shows the effectiveness of the presented IoT based SEMS.

Distributed power generation and distribution network with the dynamic pricing scheme are the major trend of the future smart grid. A smart grid is a network which contains multiple non-cooperative utility companies that offer time-of-use dependent energy prices to energy consumers and aim to maximize their own profits. Decentralized power network allows each energy consumer to have multiple choices among different utility companies. In this paper, an optimization framework is introduced to determine the energy price for utility companies in an oligopolistic energy market. At the beginning of each billing period (a day), each utility company will announce the time-of-use dependent pricing policy during the billing period, and each energy consumer will subsequently choose a utility company for energy supply to minimize the expected energy cost. The energy pricing competition among utility companies forms an n-person game because the pricing strategy of each utility company will affect the profits of others. To be realistic, the prediction error of a user's energy consumption is properly accounted for in this paper and is assumed to satisfy certain probability distribution at each time slot. We start from the most commonly-used normal distribution and extend our optimization framework to a more general case. A Nash equilibrium-based pricing policy is presented for the utility companies and the uniqueness of Nash equilibrium is proved. Experimental results show the effectiveness of our game theoretic price determination framework.

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities.

With the widespread installation of smart meters, more and more residents are signing long-term contracts with the grid for demand response (DR) scheme. As an effective way of managing energy consumption about energy prices and system energy balance conditions, the DR scheme contributes to the system cost-saving. In this context, the optimal operation of a multi-energy residential microgrid (MERMG) is studied considering the multi-energy DR scheme. The objective is to minimize the net operating cost under the system’s operational constraints. In the multi-energy DR scheme, the price-based DR (PBDR) is used for power consumption and thermal (heat or cooling) inertia is for the thermal energy consumption. Besides, to guarantee the solution quality as well as relieve the computation burden, the original nonlinear items are linearized to make the operation model a mixed-integer linear programming (MILP) problem. Then it can be then efficiently solved by commercial solvers like Cplex and Gurobi. Finally, by applying the proposed method in the test MERMG and comparing it with the one without the multi-energy DR scheme, the validity of the proposed method is verified.

Although the main concern of consumers is to reduce the cost of energy consumption, zero-energy buildings are the main concern of governments, which reduce the carbon footprint of the residential sector. Therefore, homeowners are motivated to install distributed renewable energy resources such as solar energy, which includes photovoltaics (PVs), solar concentrators, and energy storage systems (ESSs); these installations are intended to maintain the homeowners’ energy consumption, and the excess energy can be sold to the grid. In light of the comfort consumption suggestions made by users, this paper presents an optimal home energy management (HEM) for zero-energy buildings and low energy consumption. Firstly, this paper proposes a new optimization algorithm called random integer search optimization (RISO). Afterwards, we propose a new objective function to enable zero energy consumption from the grid and lower costs. Therefore, in this study, the primary energy resources for homes are PVs and ESSs, while the grid is on standby during the intermittency of the primary resources. Then, the HEM applies the RISO algorithm for an optimal day-ahead load schedule based on the day-ahead weather forecast and consumers’ comfort time range schedule. The proposed HEM is investigated using a schedule of habits for residential customers living in Hong Kong, where the government subsidizes the excess clean energy from homes to the grid. Three scenarios were studied and compared in this work to verify the effectiveness of the proposed HEM. The results revealed that the load schedule within the comfort times decreased the cost of energy consumption by 25% of the cost without affecting the users’ comfort.

In the modern world, there are numerous opportunities that help in the detection of electricity theft happening in the realm of electricity grids due to the widespread shifting of people from old metering infrastructure to advanced metering infrastructure (AMI). It is done by studying the consumers’ energy consumption (EC) readings provided by the smart meters (SM). The literature introduces a variety of machine learning (ML) and deep learning (DL) strategies to use EC data for identifying power theft in smart grids (SGs). However, the existing schemes provide low performance in terms of electricity theft detection (ETD) due to the usage of imbalanced data and using schemes in an individual manner. Moreover, the existing detectors are validated using a limited number of performance evaluation measures, which are not suitable for conducting model’s comprehensive validation. To tackle the above mentioned problems, an ML boosting classifiers based stacking ensemble model (MLBCSM) is proposed followed by adaptive synthetic sampling technique (ADASYN) in the underlying work. Data preprocessing followed by data balancing and classification are the three major parts of the model introduced in this work. Besides, the EC data acquired from the consumers’ SMs is used for detecting electricity theft. Moreover, the simulation results reveal that MLBCSM combines the benefits of adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), histogram boosting (HistBoost), categorical boosting (CatBoost), and light gradient boosting (LGBoost). Additionally, the model’s validation is ensured via different metrics. It is deduced via extensive simulations that the proposed model’s outcomes are superior to those produced by the individual models in terms of ETD.

Improving energy efficiency has become necessary to enable sustainable computational science. At the same time, scientific workflows are key in facilitating distributed computing in virtually all domain sciences. As data and computational requirements increase, I/O-intensive workflows have become prevalent. In this work, we evaluate the ability of twopopular energy-aware workflow scheduling algorithms to provide effective schedules for this class of workflow applications, that is, schedules that strike a good compromise between workflow execution time and energy consumption. These two algorithms make decisions based on a widely used power consumption model that simply assumes linear correlation to CPU usage. Previous work has shown this model to be inaccurate, in particular for modeling power consumption of I/O-intensive workflow executions, and has proposed an accurate model. We evaluate the effectiveness of the two aforementioned algorithms based on this accurate model. We find that, when making their decisions, these algorithms can underestimate power consumption by up to 360%, which makes it unclear how well these algorithm would fare in practice. To evaluate the benefit of using the more accurate power consumption model in practice, we propose a simple scheduling algorithm that relies on this model to balance the I/O load across the available compute resources. Experimental results show that this algorithm achieves more desirable compromises between energy consumption and workflow execution time than the two popular algorithms.KeywordsScientific workflowsEnergy-aware computingWorkflow schedulingWorkflow simulation