The global transition to renewable energy sources (RESs) has accelerated in an effort to reduce greenhouse gas emissions. However, the variability of RESs necessitates enhanced power grid flexibility. Simultaneously, electric vehicles (EVs), with advanced charging systems and large batteries, offer a sustainable solution, acting as flexible loads to optimize RES integration. Meanwhile, the rapid growth of cryptocurrency mining has increased ergy demand, raising concerns about grid sustainability. This study explores a building energy management system (BEMS) for a grid-connected residential building equipped with rooftop photovoltaic (PV) panels and a battery energy storage system (BESS), where the homeowner also owns an EV. Within this context, the paper introduces vehicle-to-cryptocurrency (V2C), an extension of vehicle-to-everything (V2X) technology, that integrates cryptocurrency mining devices (CMDs) into EVs. V2C enables cryptocurrency mining operations powered by surplus solar energy or stored EV battery energy. Leveraging Vehicle-to-Grid (V2G) and Vehicle-to-Building (V2B) technologies, this approach aims to reduce energy costs and promote clean energy utilization. A case study using real-world solar data from rooftop panels in Finland, evaluates the system’s effectiveness. Results show that during peak solar generation, the proposed system reduces energy costs by up to 46.19 %. Under stochastic modeling, a 12.38 % cost reduction is achieved, highlighting the economic potential of V2C. This integration offers a novel pathway to reduce EV ownership costs, accelerate their global adoption, and support sustainable cryptocurrency mining practices.

The power system faces simultaneous challenges in terms of meeting the rising energy demands, and reducing carbon emissions through the adoption of renewable energy sources (RESs) such as solar and wind. The rapid integration of RESs presents challenges associated with non-dispatchable power generation and output uncertainty. Among various energy demands, the energy consumption of cryptocurrency mining loads/farms (CMLs/ CMFs) has grown considerably on a global scale. Through an effective coordination, these loads could be strategically designed to manage the increasing uncertainties of RESs, thereby providing essential support to the power system and offering valuable grid-level services to the energy market. This paper investigates the potential of expanding CMFs to enhance system flexibility by participating in demand response (DR) programs. Using a bilevel stochastic programming approach, large CMLs are modeled as flexible and price-maker loads in both dayahead and balancing markets. At the upper level (UL), the CML owner aims to maximize expected profit by participating in the day-ahead and balancing markets, while at the lower level (LL), co-optimization of energy and reserve is determined in the electricity market clearing process. The LL objective function comprises the aggregation of day-ahead energy and reserve dispatch cost, along with expected balancing costs. The results of this study, conducted on the IEEE RTS 24-bus system, demonstrate that strategic bidding of CMLs in both dayahead and balancing markets increases profitability by 11.2% and enhances social welfare by 4.8% compared to competitive bidding strategies. Additionally, these results show that the renewable energy curtailment in the presence of these demands is significantly reduced and is approaching zero.

Virtual power plants (VPPs) strategically aggregate diverse resources to maximize profits and support independent system operators (ISOs) in managing uncertainties related to renewable energy integration. Typical VPP resources comprise generation units, demand-side participants, electrical energy storage systems, and flexible loads. Among the latter, cryptocurrency mining loads (CMLs) stand out due to their ability to provide rapid and precise flexibility services, making them valuable assets for VPPs. This paper proposes a novel VPP configuration that actively participates in day-ahead (DA) and real-time (RT) electricity markets, addressing uncertainties in wind generation and market prices. By integrating CML flexibility, the VPP gains enhanced market responsiveness and profitability, creating mutual benefits for both VPP operators and CML owners. A modified stochastic adaptive robust optimization framework is developed to achieve optimal resource utilization. The proposed approach incorporates prediction error distributions which enables more rational uncertainty budget allocation within confidence bounds. Case studies demonstrate the proposed approach’s effectiveness, showing a 6.13% profit increase at uncertainty budget of 24 for conservative asset management, with at least 4.77% improvement across all uncertainty budgets. Furthermore, an out-of-sample evaluation validates the optimization model, highlighting an additional 7.3% benefit from CML flexibility and the proposed optimization technique.

Model-based algorithms have been introduced as a practical solution for transformer protection. Compared with conventional protection functions, these methods provide more secure and dependable performance under challenging network conditions, although their high computational burden remains a limitation. This paper presents a fast model-based algorithm that not only requires lower computational burden compared with existing model-based algorithms, but also delivers comparable effectiveness. The proposed approach uses multiple linear models (MLMs) to approximate the nonlinear magnetizing characteristic of the transformer core. By replacing a single nonlinear model with several linear models, the computational burden is reduced while maintaining modeling accuracy. Simulation results have been employed to show the effectiveness of the proposed algorithm.

In a centralized protection and control substation (CPC), all of the measurements of the substation, includes voltages and currents, are available to its centralized high-performance processor. This paper proposes a model-based algorithm for protection of the power transformer in a CPC-based substation. The main difficulty in the use of conventional model-based algorithms lies in modeling of the nonlinear behavior of the transformer core. To tackle this problem, in this paper, multiple linear models are employed to simulate the nonlinearity of the magnetic core. At each time instance, the dynamic behavior of the transformer will be followed by one of these linear models and, the proposed algorithm switches between these linear models based on the concept of interactive multiple model (IMM) algorithm. This way, not only the accuracy of the proposed algorithm increases but also the computational burden is significantly reduced compared to conventional model-based algorithms. Therefore, the proposed algorithm has better potential to be implemented in real-world microprocessors. Several experimental tests include turn-to-turn (TTF) and turn-to-ground faults (TGFs) have been employed to reveal the effectiveness of the proposed IMM-based protection algorithm.

Distribution networks (DNs) are usually designed in a ring configuration but operate radially. In a typical structure, normally open points (NOs) are used to create alternative paths and supply un-faulted loads after fault clearance. A soft open point (SOP) is a power electronic device that can enhance DN performance under normal and fault conditions. However, its impact on protection algorithms has been less considered in the literature. So far, a few methods have been suggested to detect fault type and estimate fault location in the presence of SOP; however, those methods have poor performance when fault resistance is not neglected and are not effective for ungrounded DNs as well. In this paper, a new method is proposed to overcome these obstacles that significantly improves fault locating and protection performance.

In recent years, countries have increasingly promoted the deployment of renewable energy sources (RESs) to leverage their environmental and economic benefits. These incentives often take the form of power purchase agreements (PPAs), which guarantee fixed electricity prices regardless of market fluctuations. While PPAs provide steady profits for RES owners and encourage investment, they also introduce challenges within power systems. A key issue arising from these agreements is that RES owners may submit excessively low or even negative bids to secure market commitments, thereby activating PPA benefits and feeding electricity into the grid. As a result, regions with high shares of RESs may experience market clearing at negative prices. Meanwhile, cryptocurrency mining loads (CMLs) have become a significant energy consumer, accounting for nearly 1% of global electricity consumption, a share that is expected to grow. While the increasing presence of CMLs poses challenges for power grids, they can also offer valuable services, particularly in terms of demand-side flexibility. This paper proposes CMLs as highly flexible loads to counteract negative pricing. To evaluate the effectiveness of this approach, case studies were conducted, indicating that CMLs can mitigate the instances of negative prices without causing price spikes. Furthermore, the results showed that integrating CMLs into the electricity grid can reduce the curtailment of the RESs.

Although underground hydrogen storage (UHS) provides a cost-effective solution with the capacity to meet these seasonal variations, conventional energy management systems (EMSs) focus on optimizing their resource scheduling daily, which may not adequately address seasonal load or price fluctuations. Targeting these longterm fluctuations in day-ahead scheduling, This paper presents a bi-level optimization methodology tailored for UHS's operational storage strategy. The first level focuses on daily energy management over a year, whereas the second level addresses the management of yearly energy, considering the constraints specific to UHS. Using real-world data from South Australia State, simulation results confirm the effectiveness of the proposed algorithm. Also, the simulation results suggest that the hydrogen storage strategy in UHS should not solely adhere to a pre-determined, long-term scheduling. Instead, they should incorporate a short-term perspective within a long-term framework to effectively manage hydrogen storage in UHS.

The rapid proliferation of electric vehicles (EVs), if not properly integrated, has the potential to impact the secure and economic operation of distribution systems. To tackle this challenge, the system operators and planners have two primary options: i ) expanding system capacity, ii ) implementing charging management programs. In this paper, we consider an exclusive DC feeder for supplying multiple EV charging stations, connected to the main grid via the grid-tie converter. Additionally, a two-stage coordination algorithm is proposed in which the EV charging stations’ daily quotas from the grid-tie converter capacity and their day-ahead scheduling plans are determined in the first stage. In this regard, the EV charging stations determine their profit curves, and then the local market operator (LMO) collects these curves and calculates the quotas by maximizing the total profit respecting the capacity constraint. Considering the calculated quotas, the EV charging stations determine and submit their day-ahead scheduling plans to the LMO. In the second stage, each EV charging station manages its real-time operation under the uncertainties of EV parking activities. To this end, the model predictive control (MPC) method is employed to effectively handle model uncertainties. Further, to encourage EV owners’ participation in the charging control program, the EV charging station’s profit is fairly distributed among EVs based on their provided flexibility. Finally, the simulation study is provided to illustrate the effectiveness and applicability of the proposed model from the perspective of system operation and EV owners.

Cryptocurrency has become a pivotal player in global financial transactions due to its decentralized mining process, which eliminates the need for intermediaries. However, the operation of cryptocurrency mining devices (CMDs) requires significant electrical power, leading to an increase in network-connected cryptocurrency mining loads (CMLs) that pose challenges for power grid operators. This paper evaluates the integration of CMLs into power grids, focusing on distribution network performance. A technology-accepted model for assessing cryptocurrency mining performance is presented, along with an analysis of the impacts of high CML penetration on power grid planning and operation. Findings reveal that high CML penetration can complicate load forecasting, congest transmission lines, and increase the coincident peak load in distribution networks. A case study of Iran’s power network indicates that CMLs account for approximately 2.8% of the nation’s electricity consumption. Furthermore, if 5% of residential customers in Tehran install CMDs, the average coincident peak demand for this group rises by 26.81%. Additionally, the average power factor of a sample CMD is about 0.99, while total current distortion and third harmonic current exceed permissible limits, indicating potential power quality issues. This study highlights the challenges posed by CMLs and proposes strategies for their sustainable integration into power grids.

Day-ahead energy management systems focus on optimizing resource scheduling on a daily basis, which may not adequately address seasonal load or price fluctuations. Targeting these long-term fluctuations in day-ahead scheduling, this paper introduces a two-stage optimization methodology specifically designed for day-ahead scheduling with long-duration hydrogen storage systems (HSS) that effectively eliminates the need for scenario-reduction techniques by dividing the long-term scales into short-term ones. As the amount of stored hydrogen in the storage tank affects operational scheduling on consecutive days, the first stage introduces a new variable to represent variations in the stored hydrogen amount, effectively decoupling consecutive days. Subsequently, the second stage employs a developed active set algorithm. This algorithm adds hydrogen storage tank constraints to the objective function to ensure that the stored hydrogen amount does not exceed the tank’s capacity limits on any day. Using real-world data from South Australia State, simulation results validate the proposed algorithm’s effectiveness and demonstrate that employing large storage tanks within an HSS is viable for long-duration applications.

The government’s support for rooftop photovoltaic systems has significantly increased their installed capacity, leading to the creation of numerous independent microgrids (MGs). However, multi-microgrids (MMGs), which comprise these MGs, often do not share operational data with neighboring microgrids. This lack of information can lead to congestion at the common coupling point, particularly at midnight when energy prices are low, an issue that this paper addresses for the first time. This paper will break the day into two scales: daytime and nighttime. Then, to manage the congestion, it will introduce a nighttime energy storage charging market managed by an aggregator. This charging market has two levels: at the first level, each MG will optimize its energy storage charging bids, and at the second level, the aggregator will form a supply curve to settle the nighttime charging market to minimize the costs of MMGs while managing congestion. To allocate congestion costs, the aggregator will form supply and bid curves for all possible coalitions. Then based on the ’pay as cleared’ market concept, it will allocate these costs/profits using a developed Shapley value. A comparison of the proposed method with the centralized method indicates that the proposed methodology yields an optimal solution while the privacy of MGs is required. The effectiveness of this methodology is validated through a case study using real data from Australia.

Inrush current is high-magnitude current drawn by power transformers upon energization. The severity of inrush current depends on factors such as the transformer’s residual flux and the voltage phase angle at the energization instant. This paper proposes a flux matching method for the energization of V/V traction transformers to mitigate inrush current. This is achieved by adjusting the residual flux of the core to an appropriate reference value and then obtaining the proper energization instant. To this end, the method only requires knowledge of nominal voltage and excitation current, eliminating the need to acquire transformer’s parameters/design information. The railway power conditioner, typically present at the low voltage side of the V/V transformer, is used as a current source to inject sinusoidal current into the transformer windings before its energization. The reference residual flux is calculated based on the circuit breaker operating characteristics. The energization instant is determined such that the adjusted flux density matches the steady-state flux expected with respect to the applied voltage. The proposed method is validated by conducting over 14,000 simulations under different conditions using PSCAD/EMTDC. The method is also implemented and successfully tested on a laboratory-scale test rig, which verifies its effectiveness in more realistic conditions.

Inrush current refers to the high-magnitude current drawn by a power transformer upon energization. The severity of inrush current is a function of the instantaneous value of voltage at the energization instant and the transformer's residual flux density. This paper proposes an effective energization method for mitigating the inrush current of single-phase power transformers. The method does not rely on the knowledge of transformer design specifications, but the magnitude of the transformer's excitation current. The reference residual flux density is determined with respect to the limitations of the closing operation of the circuit breaker. The method then adjusts the residual flux density of the core to a value deemed appropriate by injecting controlled current into the transformer's winding. This is followed by identifying an appropriate instant for transformer energization that matches the instantaneous value of the steady-state flux density with the adjusted flux density. To validate the efficiency of the proposed method, over 8,000 simulations are conducted in PSCAD/EMTDC. The method is also implemented on a laboratory-scale testbed and extensively tested to demonstrate its effectiveness and superiority over most recent methods under a wide variety of realistic conditions.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

The protection of transmission and sub-transmission lines is conducted by overcurrent, line differential and distance protections among which the use of distance protection is very common.Mal-operation of protection equipment, including distance protection, can occur due to improper choose of the setting. This paper presents the proper method for calculation of the phase-to-phase loop resistance reach of distance protection with Quad characteristic and the impedance starter at the sub-transmission level to prevent mal-operation of the distance relay when processing its algorithm. In this work, based on the practical experiences on several industrial distance relays, it is shown that there is a possibility of improper detection of the fault loop by distance protection and then improper relay operation for out of zone faults. After review of the subject, and expressing the mathematical relations governing the problem, the appropriate phase-to-phase loop resistance reach determination method is put forward. Finally, using the information obtained from a practical incident, the efficiency of the proposed method is verified.

Recent weather-related disasters experienced worldwide with considerable damages to the interconnected power infrastructure have highlighted the importance and urgency of enhancing the resiliency of the distribution grid. A Resilient distribution grid can withstand and recover from such rare events. Resiliency against extreme events is conceptualized in three distinct stages: prior, during, and after the event. Rapid recovery is a feature of after the event stage. In this paper, restoration strategies to restore maximum loads as quickly as possible are investigated. The proposed approach attempts to restore the critical loads by using tie-switches to reconfigure the network. In the case of isolated areas without the possibility of using upstream utility grid, sectionalizing the grid into several microgrids (MGs) is proposed to improve the system resiliency. The number of isolated MGs is an issue that is required to be correctly determined. So, a new approach is proposed to compromise between amount and reliability of supplied load to find the optimum number of MGs. The proposed method is simulated on the unbalanced IEEE-123 and 37-bus distribution grid with random locations for DERs.

In recent decades, there has been a growing global focus on solar power as a renewable energy source (RES) to supply local energy demands and reduce greenhouse gas emissions. Rooftop solar photovoltaic (PV) system provides a small-scale utilization of solar energy on the roofs of apartment buildings. Investment in this system and its profitability depends on several factors, including geographic conditions, electricity price, and local load profiles. However, in Finland, the maritime and continental climates and electrically heated residential buildings present unique challenges to the investment and utilization of rooftop PV systems. Common solutions to incentivize the investment of grid-connected PV in apartments are battery energy storage systems (BESSs), demand side management (DSM), and power-to-x (P2X) approaches. Nevertheless, the value of these solutions is limited in Finland due to the seasonal variation of solar PV generation and customers’ energy consumption. This paper presents a novel and practical control and hedging mechanism to encourage investments in rooftop solar PV-BESS systems by investing in cryptocurrency mining devices (CMDs) as dispatchable and flexible loads, which facilitate the use of excess renewable energy for producing cryptocurrency, such as bitcoin (BTC). This mechanism can optimally switch the output of excessive renewable energy between exporting to the main grid and mining cryptocurrency. The proposed mechanism is studied using a dataset obtained from a residential apartment building in Helsinki, Finland, and its effectiveness is demonstrated through several practical scenarios. The results of a case study employed in this work demonstrate that the proposed hedging mechanism can provide sufficient encouragement for investors to invest in a PV system, with a return on investment equal to 57.7%. This mechanism also reduces the annual cost of residential apartments by 68.1%.

By privatization and deregulation, distribution transformers have gradually been pushed further into their operating limits. Under these circumstances, in countries with cheap or subsidized electricity price, the introduction of profitable cryptocurrencies application is deeply penetrated. It has attracted many low-voltage customers to mine these digital currencies individually or in mining pools. The employed mining devices with their own unique constant load profile have escalated the coincident factor of the loads supplied by a transformer. This can highly overload the transformers engaged with loads of this type and, in the long term will cause destructive impacts not only on the transformer also on the distribution facilities. This paper studies the adverse effect of cryptocurrency mining loads on the distribution transformer aging. At first, the customer's profit analysis through mining operation has been introduced, realizing different electric energy pricing strategies. Finally, 105 real-world distribution substations have been monitored over one year, and their actual load profiles data are recorded for analysis. A relation between the penetration rate of the mining devices and the transformer's expected lifetime is determined in this work. It is shown that the distribution transformers' expected lifetime will decrease by 25% in the presence of only a 5% penetration rate of cryptocurrency miners. This is an alarming statistic for distribution networks operating under the presence of cryptocurrency mining loads. This should be strictly addressed in the future planning of these distribution networks.