Renewable Energy Sources Output Research Articles

Waveform differential based pilot protection for transmission line connected to renewable energy sources

Affected by the control objectives of the inverter, the short-circuit characteristics of renewable energy sources are quite different from those of traditional synchronous generators. As a result, the performance of traditional protection is degraded, and may lead to mis-operation and rejection, posing hazards for the safety of the power grid. In this research, the fault characteristics of renewable energy sources and traditional synchronous generators are analyzed and compared. Then, according to the difference between the fault transient current waveforms of two power sources, a pilot protection principle based on Canberra distance is proposed. Considering the phase angle error and amplitude error during normal operation, the protection constant is theoretically set to 0.35. Simulations and field experiments show that the protection principle reliably distinguishes the faults inside and outside the zone in different scenarios. The protection is less affected by short-circuit current amplitude and abnormal data, because the numerator of Canberra distance signifies the difference between the transient current waveforms and denominator normalizes the difference. In addition, the Canberra distance is 1 when the output of renewable energy sources is 0, so the proposed protection is also applicable to the scenario where the renewable energy sources output is 0.

Flexibility dispatching strategy for power grid based on partition method of renewables penetration rate

With the proliferation of the penetration rate of renewable energy sources (RESs), more flexibility should be released to cope with the demand caused by the uncertainty of RESs output. Thus, it is paramount to divide different stages of the RESs penetration rate from the perspective of dispatching to represent the flexibility supply-demand relationship. Based on these considerations, this paper presents a novel approach to divide the level of RESs grid-connected with the aim to counteract the volatility and alleviate its effect of it. Herein, we define two specific stages with heterogeneous dispatching strategy, the first stage aiming at minimizing RESs power limit while the second stage takes the secure operation of the power grid as the optimal target by adjusting the optimization objectives of intraday rolling and real-time dispatching plan. Simulation results demonstrate the feasibility of the partition method.

Optimal Operation of Integrated Energy Systems Based on Multi-Energy Coupling With Power-to-Gas Technology

Abstract Natural gas and renewable energy sources make up an increasingly large proportion of power generation due to their being environmentally friendly. Because of the intermittent and fluctuating output of renewable energy sources, the emerging technology of power-to-gas (P2G) units is of great significance to alleviate. This paper focuses on the coordinated optimization of the combined gas and electricity network (CGEN) system with bidirectional energy conversion. Gas-fired power generation (GPG) plants and P2G are considered as linkages between the two networks. A unified CGEN mathematical model is established with the minimum operation cost as the objective function. The injection and production process of underground natural gas storage (UNGS) is also taken into consideration that is not available in other literature. Variables such as the output of P2G and gas-fired power generation plants, the supply of UNGS, and the wind curtailment are optimized correspondingly. The improved 24-node power grid (PG) and Belgium 20-node natural gas pipeline network (NGPN) are adopted to test the validity and capability of the proposed model, and then we compare the results CGEN model with the single Belgium NGPN to analyze the impact on the NGPN. Moreover, we adopt a coupled operation system of a 6-node power system and a 7-node NGPN to further analyze the influence of P2G on the CGEN. The results show that compared with the system without P2G, the total operating cost of the system is reduced by 9.39%, the natural gas load shedding is reduced by 26.1%, and the wind curtailment is reduced from 326 MWh to zero.

Optimal operation of active distribution networks hosting hybrid hydrogen‐electricity refuelling stations considering water demand under a stochastic‐IGDT approach

AbstractThis paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system.

An Intelligent Two-Stage Energy Dispatch Management System for Hybrid Power Plants: Impact of Machine Learning Deployment

The utilization of renewable energy sources such as PV and wind power has become imperative due to the increase of carbon dioxide emissions, which leads to the increase in global temperature and the negative consequences of climate change. As a result, renewable energy sources are constantly gaining popularity to be integrated in power systems to create hybrid power plants (HPPs). However, HPPs come with great complications due to the uncertainty in renewable energy output, which has given rise to the need for a reliable and effective energy dispatch management system for HPPs. In this paper, a two-stage machine learning (ML) based energy dispatch management system for HPPs is designed to control renewable energy sources (PV and wind power), reserve energy sources (energy storage systems) and backup energy sources (diesel, fuel cells, auxiliary loads, etc.). The system aims to minimize the power variance in the HPPs to achieve peak shaving and valley filling. The first stage aims to forecast the power output of renewable energy sources, as well as the load demand. The second stage aims to coordinate the energy output of the reserve and backup sources to achieve the required objective. Different ML techniques were compared to find the highest performing ML algorithm to achieve the required objective of the system, where long short-term memory (LSTM) provided the highest results with an average mean squared error of 0.005 and an average explained variance score of 0.9. The results of the management system verify the effectiveness of the system for the management of the energy dispatch in HPPs, through the successful flattening of the load curve of the HPP, which increases the reliability of the power system with the integration of renewable energy sources. Also, the system was shown to be robust against the uncertainty of the PV and wind power output, and the load demand.

Automatic Generation Control with Virtual Synchronous Renewables

As synchronous generators (SGs) are gradually displaced by renewable energy sources (RESs), the frequency stability of power systems deteriorates because RESs, represented by utility-scale solar and wind power sources, do not provide the inertial response, primary frequency response, secondary frequency response, and tertiary frequency regulation. As a result, the remaining SGs may not be sufficient to maintain the power balance and frequency stability. The concept and control strategies of virtual synchronous generators (VSGs) enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems. This paper focuses on the automatic generation control (AGC) with virtual synchronous renewables (VSRs). First, the VSR strategy that enables the RESs to participate in AGC is introduced. Second, based on the interval representation of uncertainty, the output of RES is transformed into two portions, i.e., the dispatchable portion and the stochastic portion. In the dispatchable portion, the RESs can participate in AGC jointly with SGs. Accordingly, a security-constrained economic dispatch (SCED) model is built considering the RESs operating in VSR mode. Third, the solution strategy that employs the slack variables to acquire deterministic constraints is introduced. Finally, the proposed SCED model is solved based on the 6-bus and 39-bus systems. The results show that, compared with the maximum power point tracking (MPPT) mode, VSRs can participate in the active power and frequency control jointly with SGs, increase the maximum penetration level of RESs, and decrease the operating cost.

Risks in the European Transmission System and a Novel Restoration Strategy for a Power System after a Major Blackout

Many citizens of European countries may soon experience a long and extensive blackout. The lack of predictability in the output of renewable energy sources, aggravating the problem of consistently matching supply with demand on electric grids, along with cyber-attacks or even worse unpredictable incidents in the electric grid are some factors that may mean a blackout is much more likely than in previous years. This paper covers the possibility of an extensive blackout in a country of the interconnected European electricity transmission system or, even worse, a blackout in a wide area of the European continent. The topic of this paper becomes even more important and timely given the energy crisis due to the war in Ukraine, which has made the possibility of a blackout in the winter of 2022–2023 high. First, the major European blackouts that occurred in the past 20 years are presented, examining their causes. On 8 January 2021, the European electricity grid was divided into two separate sections, with different frequencies; some additional scenarios are considered which, if they had happened, could have led to blackouts in some European countries or, even worse, a wide area of Europe. This work also examines how to avoid such an eventuality, as well as how European TSOs should react in case a blackout occurs. Focused on the fast and reliable supply of consumers after a blackout, a novel restoration strategy based on the A* Algorithm is presented. Its efficiency is validated in the IEEE-39 and IEEE-68 bus systems.

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Improved Euclidean Distance Based Pilot Protection for Lines With Renewable Energy Sources

Unique fault behaviors of renewable energy sources (RESs) may lead to the misoperation of traditional pilot protection. To cope with this issue, this article proposes a new pilot protection method using the improved Euclidean distance. For normal operation or external faults, the currents on both ends are completely opposite, so the Euclidean distance of current absolute values on both ends is equal to 0. However, it will be much larger than 0 for internal faults because transient currents on both ends will have a big difference at this time. Therefore, internal and external faults can be detected reliably. In order to facilitate the setting calculation, the Euclidean distance is normalized, and a stability factor is introduced to avoid invalid calculation results. The proposed method can be applied to different RES types and different fault ride through strategies. Meanwhile, it can withstand larger fault resistance and noise interference. Compared with other methods using the RES fault currents, this approach can operate correctly without any additional criteria when the circuit breaker recloses on a permanent fault or RESs output a low power. PSCAD simulation and real-time digital simulator experiment verify this method.

A novel cost‐effective voltage fluctuations management in a droop‐controlled islanded microgrid

This paper presents a new energy management system (EMS) for an islanded microgrid (MG) to increase power system security cost-effectively. The small size of MGs, variations in renewable energy sources (RESs) output power, and low inertia of power electronic interfaced distributed energy resources (DERs) convert voltage fluctuations into a vital issue for the MG EMS. This paper proposes a two-stage stochastic multiobjective framework to simultaneously attain economic, environmental, and technical aims. In the first stage, many scenarios related to RESs output power variations, load demand changes, and DERs contingency outages are generated using Monte Carlo simulation. Afterwards, generated scenarios are reduced and applied to the second stage. A novel objective function based on the expected voltage fluctuations (EVFs) caused by active and reactive power variations is proposed, which is minimized alongside the active and reactive costs and MG emission. The proposed model is applied to a test MG, and active and reactive power and primary and secondary reserve scheduling are correctly accomplished for five different cases. Precise analysis confirms the notability and effectiveness of MG voltage fluctuations management in energy and reserve scheduling. Numerical results show more than 60% reduction in voltage fluctuations.

Impacts of residential energy storage system modeling on power system

Power system reliability can be improved with the use of energy storage. Energy storage technologies are examined critically, including storage kinds, categorizations, and comparisons. Electrochemical and battery energy storage, thermal and thermochemical energy storage, flywheel energy storage, compressed air energy storage, pumped energy storage, magnetic energy storage, chemical and hydrogen energy storage are all taken into account. The recent research on novel energy storage kinds and its significant achievements and discoveries in energy storage are analyzed. It is the goal of this study to undertake a complete and systematic evaluation of the influence of battery energy storage systems (BESS) on power systems and microgrids. Peer-reviewed studies published between 2010 and the start of 2021 provides the basis of the SLR (Systematic Literature Review). Due to inadequate wind or sunlight, renewable energy sources (RESs) like wind and solar are regularly subjected to swings. Energy storage technologies (ESTs) help to solve the issue by storing extra energy and making it available when it’s needed. Despite the fact that there are several EST investigations, the literature is fragmented and out of date. The comparison of EST features and applications is very brief. The purpose of this article is to fill that void. It identifies major ESTs and offers an updated overview of the literature on ESTs and their potential use in the renewable energy industry, based on a set of criteria. The critical analysis reveals that Li-ion batteries have a high potential applicability in the utility grid integration sector and are BESS suited to alleviate RES volatility. However, Li-ion batteries’ costs must be decreased in order for them to be completely utilized in RES utility grid integration. It has long been shown to improve system dependability and reduce transmission costs by introducing energy storage into power networks. The development of energy storage devices is aided by regulations that promote the use of renewable energy sources rather than fossil fuels. There are also voids in this field of research. To help academics better grasp the dependability implications of energy storage systems and fill in knowledge gaps in the field, this review is available. Reduced emissions and global warming as a result of the increased usage of renewable energy resources In terms of renewable energy, wind turbines and solar PV systems are two of the most common. In contrast, the advantages and downsides of using renewable energy sources are numerous. Renewable energy’s biggest flaw is its inability to generate consistent amounts of power. It is difficult to maintain a balance between generation and demand due to the irregularity of renewable energy sources’ power output and the sudden spikes or dips in demand. Consequently, there will be deviations in grid voltage and frequency, leading to operational difficulties and perhaps jeopardizing grid stability. Battery energy storage systems (BESS) can be used to regulate the output of renewable energy sources and keep the grid stable.

Data-driven joint TEP-BESS co-planning scheme to relieve transmission lines congestion: A min-max regret method

Transmission lines congestion has recently been a vital challenge in power systems operation due to the intermittent outputs of renewable energy sources (RES). Therefore, an efficient transmission congestion management (TCM) method should be defined to deal with the congestion issue. This paper aims to propose a simultaneous linearized two-stage TEP-BESS co-planning optimization model to relieve transmission lines congestion. In doing so, a novel TCM structure is suggested in a pool-based deregulated data-driven optimal power flows (d-OPF) by the computationally effective min-max regret method to consider future scenarios of generating units and demanded loads. To improve the efficiency of the proposed algorithm, an accurate cost design for module-based BESS considering RES uncertainties is proposed; also, a regional classification is considered to simulate different weathers, specialized for the TCM measurement. The major goal is to efficiently trade-off between performing the TCM and minimizing system total cost as conflicting objectives. The output of the proposed model is to optimally find the best expansion plans by applying the computationally effective min-max regret method where performing the TCM and minimizing the system total cost. The effectiveness of the proposed model is examined on the wind-integrated regional RTS test system considering an uncertainty set for the intermittent outputs of RES. The results of this fast-converged model show the difficulty of selecting an expansion plan as the best options can be selected to relieve the transmission lines congestion while increasing the system total cost.

A Robust Economic Framework for Integrated Energy Systems Based on Hybrid Shuffled Frog-Leaping and Local Search Algorithm

The safe and efficient operation of the integrated energy system is severely hampered by a number of unpredictable elements, such as the output of renewable energy sources, the cost of energy purchases, and full demand response (IES). The effectiveness and excellence of the integrated energy system scheduling method can be increased with advanced modeling of unpredictable aspects. Thus, the IES robust stochastic optimisation model is constructed and solved with the hybrid shuffled frog-leaping and local search (HSFLA–LS) algorithm. Finally, a simulation analysis considering the uncertainty of energy purchase price based on the hybrid SFLA–LS algorithm is reduced by USD 1.63 (0.64%) and USD 3.34 (1.3%), compared to PSO and GA, respectively. In addition, the time taken to execute the SFLA–LS algorithm for the program is reduced by 1.886 s (1.59%), and 3.117 s (2.7%), compared to PSO and GA, respectively. The findings demonstrate that the suggested approach can lower system running expenses, and achieve the coordination and optimization of economy and robustness.

Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission

Sustainable energy storage medium has increased significantly in recent times. Air contamination, which is widely considered to be harmful to an ecological niche, has fuelled the growth of sustainable energy sources. On the other hand, adopting sustainable energy technology can create significant issues for keeping the grid stable. With variations in the output of renewable energy sources, storage is essential for power and voltage balancing. Storage of electricity is necessary for energy management, frequency control, peak shaving, load balancing, periodic storage, and backup production in the event of a power outage. As a result, storage technologies have received increasing attention and have evolved into something more than a need in today's world. This article provides a thorough assessment of battery energy storage systems. In addition to describing the features and capabilities of each type of battery storage technology, it also discusses the benefits and drawbacks of each innovation when contrasted to other storage mediums. There are comparative charts with many features of each storage technique provided and descriptions of the various uses of energy storage methods. Furthermore, The current work discussed the batteries' strengths, weaknesses, opportunities, and threats (SWOT) analysis in power transmission.

Day-ahead optimal scheduling of microgrid with considering demand side management under uncertainty

• Presenting a new model for day-ahead optimal scheduling of microgrids considering uncertainty by C&CG optimization algorithm. • Demand side management and flatting the electrical demand profile using customers clustering based on their consumption by C-Means algorithm. In this paper, a new model for day-ahead optimal scheduling of microgrid in the presence of energy storage system, distributed generation resources and demand side management is proposed. The proposed model consists of two layers: the clustering layer and the planning layer. In the clustering layer, the customers of microgrid are clustered based on their consumption by C-Means algorithm. In the planning layer, the objective functions minimize the operation cost and emission pollution using column and constraint generation (C&CG) optimization algorithm. Also, the electrical demand profile is flattened by optimal shifting of electrical loads. Then, the best solution available among the non-dominated Pareto solutions is selected by the fuzzy decision-making method. Moreover, the uncertain behavior of renewable energy sources output, the stochastic modelling electrical loads and electrical energy price are modelled by Monte Carlo technique. Finally, a case study is analyzed to confirm the proposed approach. Numerical results and comparison with ɛ-constraint and particle swarm optimization approaches show the proposed model capability in scheduling a uniform microgrid operation.

A model for balance responsible distribution systems with energy storage to achieve coordinated load shifting and uncertainty mitigation

An operation model is established in this paper to fully utilize the flexibility of energy storage to reduce the operation costs of balance responsible distribution systems, which are responsible for their energy balance. Depending on whether uncertainties in outputs of renewable energy sources are involved, energy storage operation is classified into load shifting and uncertainty mitigation. Such classification enables separate analysis of the two types of energy storage operation and also facilitates their coordination to achieve optimal overall costs. To handle the computational difficulty that is brought by temporal sequences of uncertainties and track uncertainties that are mitigated by energy storage, linear decision rules are adopted to model the process of uncertainty mitigation. As a flexible device, energy storage is distinct because using its flexibility influences its state of charge and eliminating such influence is essential for recovering its flexibility. Based on these features of energy storage, keys of using energy storage to mitigate uncertainties in balance responsible distribution systems are revealed, which are aggregating uncertainties from different times and thus letting them offset each other. Through case studies, it is shown that uncertainties are better mitigated when they are better aggregated, and the aggregation level of uncertainties is limited by energy storage parameters including its maximum charging and discharging power and its capacity. Besides, it is demonstrated that the cooperation between the two kinds of energy storage operation, i.e., load shifting and uncertainty mitigation, achieves better outcomes because of more efficient use of energy storage flexibility in some cases, but it is also possible that they compete for the limited flexibility of energy storage and thus need to be coordinated in other cases.

A two-stage robust optimization method based on the expected scenario for island microgrid energy management

One of the main challenges in microgrid system energy management is dealing with uncertainties such as the power output from renewable energy sources. The classic two-stage robust optimization (C-TSRO) method was proposed to cope with these uncertainties. However, this method is oriented to the worst-case scenario and is therefore somewhat conservative. In this study, focusing on the energy management of a typical island microgrid and considering uncertainties such as the power output of renewable energy sources and the power demand of loads, an expected-scenario-oriented two-stage robust optimization (E-TSRO) method is proposed to alleviate the conservative tendency of the C-TSRO method, because the E-TSRO method chooses to optimize the system cost according to the expected scenario instead, while ensuring the feasibility of the first-stage variables for all possible scenarios, including the worst case. According to the structural characteristics of the proposed model based on the E-TSRO method, a column-and-constraint generation (C&CG) algorithm is utilized to solve the proposed model. Finally, the effectiveness of the E-TSRO model and the solution algorithm are analyzed and validated through a series of experiments, and thus obtaining some important conclusions, i.e., the economic efficiency of system operation can be improved at about 6.7% in comparison with the C-TSRO.

Energy financing in COVID-19: how public supports can benefit?

PurposeThe study aims to empirically estimate the role of public supports for energy efficiency financing and presents the way forward to mitigate the energy financing barriers that incurred during the COVID-19 crisis.Design/methodology/approachUsing the G7 countries data, the study estimated the nexus between the constructs. Generalized method of moments (GMM) and conventional increasing-smoothing asymptotic of GMM are applied to justify the study findings. Wald econometric technique is also used to robust the results.FindingsThe study findings reported a consistent role of public support on energy efficiency financing indicators, during the COVID-19 crisis period. G7 countries raised funds around 17% through public supports for energy efficiency financing, and it raised 4% of per unit energy usage to GDP, accelerated 16% energy efficiency and 24% output of renewable energy sources, during COVID-19. By this, study findings warrant a maximum support from public offices, energy ministries and other allied departments for energy efficiency optimization.Practical implicationsThe study presents multiple policy implications to enhance energy efficiency through different alternative sources, such as, on-bill financing, direct energy efficiency grant, guaranteed financial contracts for energy efficiency and energy efficiency credit lines. If suggested policy recommendations are applied effectively, this holds the potential to diminish the influence of the COVID-19 crisis and can probably uplift the energy efficiency financing during structural crisis.Originality/valueThe originality of the recent study exists in a novel framework of study topicality. Despite growing literature, the empirical discussion in the field of energy efficiency financing and COVID-19 is still shattered and less studied, which is contributed by this study.

A Two-Stage Robust Optimization Method Based on the Expected Scenario for Islanded Microgrid Energy Management

One of the main challenges in microgrid system energy management is dealing with uncertainties such as the power output from renewable energy sources. The classic two-stage robust optimization (C-TSRO) method was proposed to cope with these uncertainties. However, this method is oriented to the worst-case scenario and is therefore somewhat conservative. In this study, focusing on the energy management of a typical islanded microgrid and considering uncertainties such as the power output of renewable energy sources and the power demand of loads, an expected-scenario-oriented two-stage robust optimization (E-TSRO) method is proposed to alleviate the conservative tendency of the C-TSRO method because the E-TSRO method chooses to optimize the system cost according to the expected scenario instead, while ensuring the feasibility of the first-stage variables for all possible scenarios, including the worst case. According to the structural characteristics of the proposed model based on the E-TSRO method, a column-and-constraint generation (C & CG) algorithm is utilized to solve the proposed model. Finally, the effectiveness of the E-TSRO model and the solution algorithm are analysed and validated through a series of experiments, thus obtaining some important conclusions, i.e., the economic efficiency of system operation can be improved at about 6.7% in comparison with the C-TSRO results.

Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks

By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system.