Smart Grid Strategies Research Articles

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

A situated governmentality approach to energy transitions: technologies of power in German and Indian smart grid strategies

Abstract. Around the world, smart grids are emerging as a universal tool to address a wide range of social and technical problems facing energy systems. Despite considerable research on these systems, the ways they differ in the local (re)production of power relations have so far been little discussed. This paper fills this gap by developing a “situated governmentality approach” in conversation with the critique of Foucauldian governmentality studies. By applying this approach to smart grid strategies in Germany (Smart Energy Showcases – Digital Agenda for the Energiewende, SINTEG) and India (National Smart Grid Mission, NSGM), we identify different ways in which power is mediated through situated governmentalities. While SINTEG employs technologies of power that promote a disciplinary regime, the exercise of power in the case of the NSGM displays many elements of a digitally enhanced sovereign approach. The findings reveal the range of governmental programmes that can be realized through smart grids and open up a perspective on the situated functioning of smart grids in energy transitions.

A Multi-Agent Framework for Smart Grid Simulations: Strategies for power-to-heat flexibility management in residential context

The complexity of a system, such as the Smart Grid, comes from the interaction of different fields of expertise and actors cooperating together. As a result, co-simulation frameworks are emerging to meet the need for analysis from a broader perspective. The feature to better understand the Smart Grid environment is the ability to simulate its different components simultaneously. Therefore, we propose a multi-agent co-simulation framework that can serve as a test-bed for multiple Smart Grid strategies. In particular, we focus on Demand Response programs that exploit the thermal behaviour of residential buildings. The proposed framework is modular to test Plug&Play models. Moreover, it is highly flexible and configurable to evaluate realistic scenarios. We tested the platform on a case study of 1,000 buildings, performing an analysis of the effects of micro temperature deviations in buildings on the primary grid substation balancing problem. The results show the flexibility of the platform in testing different strategies. Moreover, power imbalances could be mitigated simply by acting on the indoor temperature set-points with smaller deviations.

Blockchain-based multi-authority revocable data sharing scheme in smart grid.

In view of the problems of inefficient data encryption, non-support of malicious user revocation and data integrity checking in current smart grid data sharing schemes, this paper proposes a blockchain-based multi-authority revocable data sharing scheme in the smart grid. Using online/offline encryption technology with hybrid encryption technology enhances the encryption performance for the data owner. The use of user binary tree technology enables the traceability and revocability of malicious users. The introduction of multiple attribute authorization authorities eliminates the threat of collusive attacks that exist in traditional data-sharing schemes. In addition, the semi-honest problem of third-party servers is solved by uploading data verification credentials to the blockchain. The security analysis results show that the scheme can resist selective plaintext attacks and collusion attacks. The performance analysis results show that the proposed scheme has lower computational overhead and better functionality than similar schemes, which is suitable for secure data sharing in smart grids.

A cost-efficient DC active load laboratory solution

The increased use of DC renewable energy resources and DC storage systems, combined with the necessary reduction of energy waste, is boosting the development of DC smart grids. In this scenario, DC load emulation is of great importance. From the hardware point of view, DC buses stability of smart grids and the different DC/DC converter topologies must be tested. From the software point of view, smart grid strategies and job schedulers must be tested with different power absorption profiles. Moreover, DC load emulation can be useful for many other purposes, such as battery characterization, power supply testing, photovoltaic I-V curve measurements, etc. In this work, a cost-efficient DC Active Load (AL) solution is proposed. The principle of the circuit topology is a buck-boost-derived converter. This solution can be designed and tested considering the required voltage, current, and maximum input power. Both simulation and experimental results are shown on a 400 W size prototype. Thermal and electrical results validate the simulation model and the AL feasibility.

Efficient Function Queryable and Privacy Preserving Data Aggregation Scheme in Smart Grid

The collection of users' near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.

Fog-enabled secure multiparty computation based aggregation scheme in smart grid

The Smart Grid (SG) is a modern power grid infrastructure for improved efficiency and reliability using modern telecommunications infrastructure, sensing, and measuring technologies. It provides sustainable electricity to customers and enables them to monitor their electricity use. The Fog Nodes (FNs) are deployed between the Cloud Control Center (CCC) and Smart Meters (SMs) to improve efficiency and reliability. The CCC is collecting usage data through FNs at regular intervals from SMs. As SM data contains private customer data, a secure scheme is required to ensure customer privacy during data collection. The usage view behavior of consumers is only possible if internal or external malicious entities collude together. In this paper, we present the Fog-Enabled Secure Multiparty Computation (FESMPC) based aggregation scheme for SG. This paper’s key contributions to the literature on the topic of secure privacy-aware aggregation schemes in SGs are threefold. Firstly, a secure aggregation scheme that preserves data privacy and ensures fault-tolerance is presented. The scheme is robust against the attacks of collusive and False Data Injection (FDI). A collusive attack is managed through enhanced Shamir’s secret scheme. At the same time, privacy during data aggregation is achieved through a Paillier homomorphic encryption scheme. Secondly, detailed security analyses are provided to show that the proposed scheme supports data privacy, source authentication, fault tolerance, and prevents FDI attacks. Thirdly, the paper presents extensive performance evaluations demonstrating that the proposed program scheme is efficient in encryption, aggregation, decryption, and less communication costs than the three existing state-of-the-art programs.

Multiauthority Traceable Ring Signature Scheme for Smart Grid Based on Blockchain

As the next‐generation power grid system, the smart grid can realize the balance of supply and demand and help in communication security and privacy protection. However, real‐time power consumption data collection might expose the users’ privacy information, such as their living habits and economic conditions. In addition, during the process of data transmission, it may lead to data inconsistency between the user side and the storage side. Blockchain provides tamper‐resistant and traceable characteristics for solving these problems, and ring signature schemes provide an anonymous authentication mechanism. Therefore, in this work, we consider the applications of ring signature scheme in smart grid based on blockchain. We introduce the notion of multi‐authority traceable ring signature (MA‐TRS) scheme for distributed setting. In our scheme, there is an auditing node that can distinguish the identity of the real signer from the ring without any secret information. Last but not least, we prove that the proposed scheme is unforgeable, anonymous, and traceable.

Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach

Unbalanced power demand across time slots causes overload in a specific time zone. Various studies have proved that this can be mitigated through smart grid and price policy, but research on time preference is insufficient. This study proposed a real-time pricing model on a smart grid through a two-stage Stackelberg game model based on a utility function that reflects the user’s time preference. In the first step, the suppliers determine the profit-maximizing price, and then, the users decide the electricity usage schedule according to the given price. Nash equilibrium and comparative analysis of the proposed game explain the relationship between time preference, price, and usage. Additionally, a Monte Carlo simulation demonstrated the effect of the change in time preference distribution. The experimental results confirmed that the proposed real-time pricing method lowers peak-to-average ratio (PAR) and increases overall social welfare. This study is meaningful in that it presents a pricing method that considers both users’ and suppliers’ strategies with time preference. It is expected that the proposed method would contribute to a reduction in the need for additional power generation facilities through efficient operation of the smart grid.

Renewable energy based self-healing scheme in smart grid

The renewable energy based smart grid present a stable power supply system with low carbon emissions. The adaptability of work in smart grid-related approaches allows microgrids to load reliably. This research proposes a self-healing method with a large smart grid in different purpose. The proposed technologies include re-dispatch generation, reconfiguration organizations, and load restrictions. Smart network self-healing problems are defined as integer quadratic problems. This paper presents a flexible model solution to promote a horizontal generation. Innovation settings reliability of electrical reliability smart grid modification is used to instruct reproduction in various circumstances. The results of recreation show the ability of the proposed self-healing scheme because it reduces the cost of load outages and generation with ideal exchange activities. The proposed system demonstrates self-healing capabilities to ensure smart network security by constantly preventing fault and adaptation planning generations. Simulation results from the (Western Systems Coordinating Council) WSCC 3 generator system adjusted with the microgrid module confirm the power system of the proposed structure.

A practical group blind signature scheme for privacy protection in smart grid

The leakage of privacy is one of the key factors to restrict the development of smart grid. Currently, research works of protecting privacy in smart grid mainly focused on two aspects: (1) data aggregation based on the mathematical model and algorithm and (2) user anonymous authentication. However, data aggregation is at cost of obtaining fine-grained electricity consumption information to protect privacy. Anonymous authentication cannot identify the malicious user in previous studies. Hence, in this work, we propose a group blind signature scheme in smart grid to accomplish conditional anonymity. Furthermore, the integrity of consumption data can be verified by homomorphic encryption (HE) which can decrease the communication overhead between control center and smart meter remarkably. From the security analysis and experiment simulation, the results show that our scheme is safe and efficient.

Prominent smart grid measurements by RAP-NN to warrant the reliability of the PV system

ABSTRACT Nonetheless, the PV system evolves a clear fuel source, is a fluctuating source of energy subsequently it has to ensure its reliability, which can be got through the Knowledge about the reliability sensing parameters such voltage, current, power factor. But when it comes to sense knowledge about the reliability parameters of the PV system over the large cover area there arise the recurrence problem. Therefore, this scenario forces the PV system to have reliability recurrently sensing prominent smart grid strategy. Hitherto, those recurrent inputs are being clustered and the parameters have been sensed but the accurate measuring is lacking due to the squander of essential data and due to the increased memory utilization for recurrence operation. Regarding this issue, this work presentedAdvanced Artificial intelligent RAP-NN (Recurrent Approximated Predictive – Neural Network) for Prominent Smart Grid Measurements which avoids increased utilization of memory by including Long Short Term Memory for recurrently updating values and also the accurate measurement of the parameters are done by fourth-order Runge-Kutta approximation strategy which in turn gives very accurateprediction parameters readily yields better power quality measurement.

A DNA-based Privacy-preserving Scheme in Smart-grid

A DNA-based Privacy-preserving Scheme in Smart-grid

A DNA-based Privacy-preserving Scheme in Smart-grid

Smart grid utility provider collects consumers’ power consumption data for three main reasons: billing, analysis, and operation. Billing needs coarse-grained data where there are no, or minimal, privacy concerns. While analysis and operation needs fine-grained data which can highly explore consumers’ privacy. Hence, consumers might be reluctant to allow for operational metering to protect their privacy.This paper presents detail description of a reliable DNA-based privacy-preserving (DNAPP) scheme in smart grid. DNAPP assures robust authentication, confidentiality, message integrity, and nonrepudiation across the smart grid as well as assuring high consumers’ privacy. The scheme demonstrates many good security features, such as: high complexity of O(n!), light-weight, scalable, minimum overhead, no cryptography key exchange between the communicating parties as each of them can determine the key locally and independently. This scheme does not require any level of modifications to the existing smart grid infrastructure or smart meter. It only requires some software modifications.

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

Efficient Privacy-Preserving Multi-Dimensional Data Aggregation Scheme in Smart Grid

Smart grid, characterized by high efficiency, security, and flexibility, is gradually replacing the traditional power grid. Data aggregation technology is frequently used to avoid user privacy disclosure as a result of power consumption data transmission in the smart grid. However, traditional one-dimensional data aggregation schemes fail to meet the demands of fine-grained analysis. Therefore, this paper proposes an efficient privacy-preserving multi-dimensional data aggregation (P 2 MDA) scheme in smart grid by virtue of homomorphic encryption and superincreasing sequence. The security analysis indicates that the proposed scheme is proved to be secure in the random oracle model, satisfying all security and privacy requirements. The extensive performance analysis shows that in comparison to the related schemes, the proposed scheme achieves lowest computation and communication costs, thus appropriate for practical applications.

Energy-efficiency-oriented scheduling in smart manufacturing

Energy efficiency is one of the key drivers for improving the efficiency of resource utilization in smart manufacturing. Within the manufacturing environments, the importance of energy efficiency has grown. However, in most cases the energy consumption of the manufacturing systems are considered using average energy consumption models or simple scheduling models for the needs of discrete event simulation. This paper researches on energy-efficiency scheduling based on the scenario of multi-tasks and multi-resources in smart manufacturing. The scheduling problem is analyzed and modeled as a mixed integer linear programming (MILP) problem with constraints, such as the feasibility of time domain or space, qualification and initial state. A solution of the mixed integer linear programming which optimized by a fast algorithm based on score ranking is designed under time of use (TOU) electricity pricing scheme in smart grid, in which the electricity price varies throughout a day. The feasibility of the method is verified by simulation case. Under the premise of ensuring the reasonable allocation of manufacturing tasks, the goal of smart manufacturing process and long-term energy optimization would be achieved.

C2C: Community-Based Cooperative Energy Consumption in Smart Grid

In this paper, a community-based cooperative energy consumption scheme in smart grid, which alleviates energy consumption cost to customers, is proposed. The concept of community among customers in the smart grid is discussed. To form different communities among customers, a community-based game among customers is orchestrated, while considering the dynamic nature of the composition of the community. A practical scenario involving multiple customers forming a group and cooperating with one another is considered. The proposed dynamic community formation scheme always achieves an equilibrium state. Furthermore, the proposed scheme also helps to reduce peak-to-average ratio of the energy demands from the customers in different time periods. Simulation results show that the proposed cooperation-based scheme outperforms the existing schemes. It is also shown that customers can minimize their energy consumption cost by approximately 16% using the proposed scheme, compared to noncooperative approaches.

Comparative Study Improving Residential Load Factor Using Power Shifting and Load Shifting

One the problem of electric power stability is due to load fluctuations in distribution system especialy during peak load conditions. One solution which is applied in Smart Grid scheme is through load shifting or power shifting. In load shifting the load with high power consumption which operates at peak load shifted its operations outside of the peak load or s some loads with high power consumption do not operate at the same time, there is a load whose operating time is shifted to reduce peak power. Power shifting is used when energy from renewable source not directly suplied to load but storage first and supplied to reduce high power consumption to reduce peak power. Low load factor, ratio between average power to peak power, may affect to power system operation. If load factor of residential load can keep in low, it will be certainly help improve the stability of the power system. In this study we will examine the comparison of load shifting method with power shifting in improving load factor. Load shifting is done to water pumps and washing machines, because washing machine is shiftable load. Power shifting is made to the output power of the solar power plant, which is used to reduce peak power from the water pump. Test results show that power shifting can increase load factor value up to 54,9% while load shifting give load factor value equal to 43,9%.