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Interest in green energy has increased worldwide. Therefore, smart grid projects to form a more efficient and eco-friendly intelligent grid by combining information technology (IT) technology with the existing grid are actively being conducted. In Korea, a national-level smart grid project road map has been confirmed, and an action plan has been prepared. Despite such actions, there may appear various threat scenarios in the application of the IT to the grid as a reverse function. Security technology is a measure to respond to such threats effectively. The security technology of a smart grid is an important factor that is directly related to the success or failure of the smart grid project. A smart grid is a new type of next-generation grid born of the fusion with IT. If the smart grid, the backbone of the power supply, is damaged by a cyberattack, it may cause huge damage, such as a nationwide power outage. In fact, there is an increasing cyberattack threat, and the cyber security threat to the smart grid is not insignificant. Furthermore, the legal system related to information protection is also important in order to support it systematically. In this paper, the necessity of the smart grid is examined, and the industry’s initiative toward the smart grid security threat and threat response is examined. In this paper, we also suggest a security plan of applying Rainbowchain, the Blockchain technology, to the smart grid and energy exchange. We propose achieving superior performance and security functions by using Rainbowchain, which contains seven authentication techniques among existing Blockchain technologies, and propose the ecosystem and architecture necessary for its application.

Power grids in the 21st century face unprecedented challenges, including the urgent need to combat pollution, mitigate climate change, manage dwindling fossil fuel reserves, integrate renewable energy sources, and meet greater energy demand due to higher living standards. These challenges create heightened uncertainty, driven by the intermittent nature of renewables and surges in energy consumption, necessitating adaptable demand response (DR) strategies. This study addresses this urgent situation based on a statistical analysis of recent scientific research papers. It evaluates the current trends and DR practices in buildings, recognizing their pivotal role in achieving energy supply–demand equilibrium. The study analysis provides insight into building types, sample sizes, DR modeling approaches, and management strategies. The paper reveals specific research gaps, particularly the need for more detailed investigations encompassing building types and leveraging larger datasets. It underscores the potential benefits of adopting a multifaceted approach by combining multiple DR management strategies to optimize demand-side management. The findings presented in this paper can provide information to and guide future studies, policymaking, and decision-making processes to assess the practical potential of demand response in buildings and ultimately contribute to more resilient and sustainable energy systems.

International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.

The increased penetration of renewables is beneficial for power systems but it poses several challenges, i.e., uncertainty in power supply, power quality issues, and other technical problems. Backup generators or storage system have been proposed to solve this problem but there are limitations remaining due to high installation and maintenance cost. Furthermore, peak load is also an issue in the power distribution system. Due to the adjustable characteristics of loads, strategies on demand side such as demand response (DR) are more appropriate in order to deal with these challenges. Therefore, this paper studies how DR programs influence the operation of the multi-microgrid (MMG). The implementation is executed based on a hierarchical energy management system (HiEMS) including microgrid EMSs (MG-EMSs) responsible for local optimization in each MG and community EMS (C-EMS) responsible for community optimization in the MMG. Mixed integer linear programming (MILP)-based mathematical models are built for MMG optimal operation. Five scenarios consisting of single DR programs and DR groups are tested in an MMG test system to evaluate their impact on MMG operation. Among the five scenarios, some DR programs apply curtailing strategies, resulting in a study about the influence of base load value and curtailable load percentage on the amount of curtailed load and shifted load as well as the operation cost of the MMG. Furthermore, the impact of DR programs on the amount of external and internal trading power in the MMG is also examined. In summary, each individual DR program or group could be handy in certain situations depending on the interest of the MMG such as external trading, self-sufficiency or operation cost minimization.

Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW),$16,235 ($ 528,825 −$512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $ 317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.

Integrated energy systems (IESs) can realize the conversion and complementarity of various energy sources, which provides opportunities and challenges for the energy market. Considering that the user’s energy consumption is affected by the energy price difference, there is a problem that the new energy output in the comprehensive energy system does not fully match the user’s energy demand period. In order to solve the above problems, this paper proposes a two‐stage optimization model of “open source and reducing expenditure” to give full play to the potential of multiple energy sources on the load side to participate in demand response (DR) and combine low‐carbon technology and market mechanisms to realize the low‐carbon economic operation of the comprehensive energy system. In the first stage, a collaborative optimization strategy for electric and thermal DR is constructed from the aspect of “reducing expenditure,” a comprehensive load fuzzy DR mechanism based on the logistic function is constructed for electric load, and the load curve and time‐of‐use (TOU) energy price are optimized considering the coupling characteristics of user energy consumption, and nondominated sorting genetic algorithm (NSGA‐II) solution to achieve peak shaving and valley filling. In the second stage, a joint operation model of carbon capture power plant (CCPP) and power‐to‐gas (P2G) equipment is built from the aspect of “open source,” and the ladder‐type carbon trading mechanism is considered to rationalize the unit output and achieve low‐carbon emission reduction. The calculation results obtained through examples show that the total cost of the model is slightly reduced by 5.44%, but the actual total carbon emission of the system is greatly increased by 50.73%. It proves that the high‐carbon power plant transformation and TOU energy price optimization strategy are effective for the low‐carbon economic operation of the system and realize both economic benefits and benefits of the system.

Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.

The explicit demand response (DR) is a key program for reinforcing the participation of end customers and making the most out of the potential of the smart grid. The DR is a key topic in the field of buildings to make use of the flexibility that they can offer. However, in order to guarantee the correct functionality of a DR system, it is fundamental to perform interoperability tests among the various components/actors. In this paper, we take into consideration the technological solutions suggested in the framework of the DRIMPAC project to enable the DR in buildings. We consider all actors/devices involved in order to reach the objective of executing a flexibility order by an asset. Following a structured interoperability testing methodology created by the Joint Research Centre, we perform interoperability tests regarding all critical links of the full chain of interacting actors to obtain the DR in buildings. The results show that the system functions properly and the benefits from the DR can be exploited. On the other hand, we provide a concrete example of how to apply the interoperability methodology in the field of testing the DR in buildings.

Natural gas is a fossil fuel that has been widely used for various purposes, including residential and industrial applications. The combustion of natural gas, despite being more environmentally friendly than other fossil fuels such as petroleum, yields significant amounts of greenhouse gas emissions. Therefore, the optimization of natural gas consumption is a vital process in order to ensure that emission targets are met worldwide. Regarding residential consumption, advancements in terms of boiler technology, such as the usage of condensing boilers, have played a significant role in moving towards this direction. On top of that, the emergence of technologies such as smart homes, Internet of Things, and artificial intelligence provides opportunities for the development of automated optimization solutions, which can utilize data acquired from the boiler and various sensors in real-time, implement consumption forecasting methodologies, and accordingly provide control instructions in order to ensure optimal boiler functionality. Apart from energy consumption minimization, manual and automated optimization solutions can be utilized for balancing purposes, including natural gas demand response, which has not been sufficiently covered in the existing literature, despite its potential for the gas balancing market. Despite the existence of few research works and solutions regarding pure gas DR, the concept of an integrated demand response has been more widely researched, with the existing literature displaying promising results from the co-optimization of natural gas along with other energy sources, such as electricity and heat.

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.