Renewable Distributed Energy Resources Research Articles

A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices

This article develops a stochastic multi-level multi-objective strategy to facilitate the electricity market clearing process among micro-grids considering the environmental impacts of fossil fuel powered generation units. Various types of distributed renewable energy resources (DRERs), plug-in electric vehicles (PEVs) and internet-of-things (IoT)-enabled smart homes (SHs) are considered in the proposed concept. In addition, energy storage devices (ESDs) are installed in SHs, micro-grids and distribution network to handle the negative impacts of operational uncertainties arising from DRERs and load demand. Scenario-based stochastic (SBS) method is adopted to model the scenarios of uncertain parameters. In the proposed methodology, micro-grids build their bids/offers for market participation by using the weighted fuzzy method with regard to the SHs optimal programs and aiming to minimizing operational and emission costs. SHs have PEVs and ESDs, and control their controllable appliances via the IoT. The proposed methodology is formulated in linear form and is implemented on a modified IEEE 33-node distribution network comprising three micro-grids. The simulation results prove that the IoT infrastructure in SHs has led to their effective participation in the electricity market and subsequently lowering their costs by 9.13 %. Besides, the results reflect that the provision of vehicle-to-grid services by PEVs along with the optimal operation of ESDs not only lowers the market settlement price (MSP) by about 25 % during the high-demand period, but also reduces daily operation costs and carbon emissions by 32.85 % and 27.65 %, respectively.

A CURRENT RETENTION BASED PROTECTION ALGORITHM FOR DC MICROGRIDS

Microgrids have been identified as a step towards goals of global green energy generation as they offer attractive options of renewable resource inclusion in decentralized energy networks, thus providing incentive towards meeting a booming energy demand sustainably. They are however impaired by the characteristic nature of Distributed Renewable Energy Resources (DRERs). While DRERs and microgrids offer the advantage of sustainable energy generation and autonomous operation with respect to the traditional grid, their intermittency and unconventional characteristics due to deviation from the traditional power grid structures causes trepidation while opting for them. A cause of concern while employing microgrids in daily use is the peril to personnel and equipment during the occurrence of a fault. To mitigate severe loss of life and property, it is important to develop and design protection algorithms for microgrids. While there is a comparatively large pool of knowledge on AC microgrid protection, DC microgrid protection is challenging and is being focused on by researchers around the world. The unorthodox nature of these networks cause conventional protection algorithms to be unsuitable and make the protection of these microgrids tasking. The work in this paper aims to contribute to efforts in the protection of hybrid microgrids. While the work in this paper is limited to the DC side of the grid, the proposed algorithm is able to detect and identify the location of various types of DC faults. The algorithm is verified on a secondary radial hybrid microgrid and is further compared with existing DC protection algorithms on various performance parameters.

State-of-the-Art Literature Review of Power Flow Control Methods for Low-Voltage AC and AC-DC Microgrids

The development of AC distribution systems provides for the seamless integration of low-voltage microgrids with distributed energy resources (DERs). This poses new challenges for the control of normal, emergency, and post-emergency states of microgrids, calling for the creation and development of information and communications technology infrastructure. Power converters/inverters that are used to integrate renewable DERs lack inertia. Along with them, fossil fuel-fired generation units are also being integrated into microgrids. These include gas generator sets, diesel generator sets, and microturbines, having small (up to 1–2 s) values of mechanical inertia constants—Tj. This leads to an increase in the rate of transients by a factor of 5–10. Under these conditions, the technical requirements for the speed of automatic power flow control systems, as well as the methods they rely on, have to be reconsidered. Microgrids include DC microgrids, AC microgrids, and hybrid (AC-DC) microgrids. In the case of hybrid microgrids, DERs are connected to the DC grid and are integrated into the AC grid through a common inverter. The complexity of the task of microgrid control is due to the need to choose properly the type and extent of control actions so as to prevent the emergence and development of accidents. The employed control methods must ensure the reliable power supply to consumers and the quality of power in microgrids, as well as the reliable operation of the external distribution systems into which they are integrated. The article gives an overview of control methods for low-voltage AC and AC-DC microgrids, which allow one to tackle effectively solve the tasks.

Subordinated Gaussian processes for solar irradiance

AbstractTraditionally the power grid has been a one‐way street with power flowing from large transmission‐connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by‐the‐second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non‐stationary and non‐Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non‐Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

Impact of implementing emergency demand response program and tie-line on cyber-physical distribution network resiliency

Recently, due to the complex nature of cyber-physical distribution networks (DNs) and the severity of power outages caused by natural disasters, microgrid (MG) formation, distributed renewable energy resources (DRERs), and demand response programs (DRP) have been employed to enhance the resiliency of these networks. This paper proposes a novel multi-objective MGs formation method-based darts game theory optimization algorithm. The microgrid formation is obtained by controlling the sectionalizing and tie-line switches. The network graph theory is used to represent the constructed microgrid, and the non-linear equations of power flow and loss calculations are adopted in the microgrid formation model. To measure the system's resiliency under extreme disaster events, metrics are utilized to prove the system's flexibility and resiliency. The modified IEEE 33-bus test system is designed to validate the proposed approach's effectiveness. Three case studies are performed with and without considering the emergency demand response program (EDRP) and tie-lines.

Low-Cost Communication Interface between a Smart Meter and a Smart Inverter

The need for a low-cost interface between the grid and small (<250 kW) renewable distributed energy resources (DERs) is growing in importance as the number of small DERs continues to grow. In this study, a system architecture was proposed to investigate paths to an affordable interconnection for small renewable DERs.Then, a low-cost communication interface between a smart meter and smart inverter was installed using a commercially available bridge device. The interface device was selected based on an assessment concluding that it would be able to support the emerging advanced metering infrastructure (AMI) network. Next, messages were passed across the experimental end-to-end communication interface to test their speed and reliability. Success was based on whether the key functions defined in the standard IEEE 2030.5 were executed or not, which include set points, disconnect/reconnect, and Volt-VAr optimization. The results of the testing provided detailed insights into the benefits and limitations of the proposed architecture. Intermittency of weather-dependent DERs (e.g., solar and wind) adversely impacts the power quality of a DER, making hourly day-ahead prediction nearly impossible. With this in mind, the investigation also considered the potential of using smart inverter functions to reduce DER’s intermittency.

P2P Multigrade Energy Trading for Heterogeneous Distributed Energy Resources and Flexible Demand

This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks. In particular, a reliability credit (RC) assignment method is developed for customers to differentiate the energy grades considering the heterogeneity in energy supplying reliability of DERs and the consumption preferences of customers. Later, an innovative P2P multi-grade energy trading model is introduced where different types of demand are matched up with the corresponding grades of energy. The market clearing is modeled as a social welfare maximization problem considering the net utilities of customers and the profits of DER producers. Furthermore, a fast decentralized pricing algorithm is developed that achieves the maximum of social welfare and also preserves the privacy of individual participants. To substantially improve the computational efficiency, a fast solution method is devised for customers to solve the optimal energy procurement combination problem, and closed-form solutions are derived for DER producers to allocate energy optimally. Numerical tests on a 69-node distribution feeder validate the superiority of the proposed P2P multi-grade energy trading scheme compared to the benchmark scheme. The high efficiency and scalability of the proposed fast pricing algorithm are corroborated by the simulation on the IEEE 8500-node distribution system.

Optimal microgrid programming based on an energy storage system, price-based demand response, and distributed renewable energy resources

Microgrids (MGs) in distribution systems can be operated in far regions at lower investment costs using renewable distributed energy resources (DERs). The present paper introduces a stochastic model for optimal energy-heat programming and the daily storage of an MG. Bi-level stochastic programming is presented for integrated energy-heat scheduling and storage in the presence of an energy storage system (ESS) and demand response (DR) based on social welfare maximization. Out of the incentive-based DR programs, the tender and redemption and the ancillary services market programs were selected and applied to the given model. Besides, the time of use (TOU) -based DR and real-time pricing (RTP) were considered as the price-based demand response (PBDR) programs in optimal programming. The PBDR programs have been included in the objective function using a linear function based on consumer benefits. The proposed bi-level stochastic model was solved using a developed metaheuristic optimization algorithm called the lightning search algorithm (LSA) in the present work. The Latin hypercube sampling (LHS) and KMEANS methods were used to produce and reduce the scenario. The proposed framework was investigated in a 33-bus test model. The obtained simulation results were evaluated from different aspects. The TOU and RTP effects and ESS are shown in obtained numerical analysis by considering the operating cost, total social welfare, and the client's utility function.

Integration of CCHP microgrids in NZEB with critical loads under high PQR requirements, a position paper

In recent years, many projects have been developed to reduce the energy consumption of buildings, both from the point of view of energy efficiency and the integration of renewable energies. However, few projects are related to the problem of integrating DER in environments dominated by high-tech equipment, the so-called ”critical loads”: data centers, railroad stations, airports, and hospitals. The European Interreg Sudoe IMPROVEMENT project aim is to renovate existing public buildings where critical loads predominate, converting them into nearly zero-energy buildings (NZEB), and for this purpose, it integrates combined cooling, heat, and power (CCHP) microgrid with renewable and other distributed energy resources (DER) like hybrid energy storage systems (ESS). With still one year to go before the end of the project, the consortium partners present in this position paper the latest progress of their respective work packages to date.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of the power peaks is a new objective and open issue to a well-functional EMS, along with the maximization of profit in the energy market. Thus, both objectives have to be taken into account at the same time. Thus, this paper proposes the EMS application incorporating power offering strategy applying a nature-inspired algorithm such as Particle Swarm Optimization (PSO) algorithm, in order to find the optimal solution of the objective function in the context of the overall operating cost, the coordination of DERs, and the energy losses in a MG integrated VPP network. For a fair DERs coordination with minimized power fluctuations in the power flow, the power offering strategies with an active power control and re-distribution are proposed. Simulation results show that the proposed MG integrated VPP model with PSO-based EMS employing Egalitarian reDistribution (ED) power offering strategy is most feasible option for the overall operating cost of VPP revenue. The total operating cost of the proposed EMS with ED strategy is 40.98$ compared to 432.8$ of MGs only without EMS. It is concluded that each MGs in the proposed VPP model intelligently participates in energy trading market compliant with the objective function, to minimize the overall cost and the power fluctuation.

Placement of Renewable Distributed Energy Resources in the Radial Distribution Network to Overcome the Losses and Air Pollution

Unplanned placement of the distributed energy resources in the existing network can cause severe problems like voltage instability, increase in power losses, system islanding, reverse power flows, air pollution, etc. Power losses and voltage profile maintenance are the most significant restrictions of the existing power system. Therefore, optimal placement of distributed energy resources is required to overcome the above problems and the use of renewable distributed energy resources is required for the reduction of air pollution. For optimal placement, many researchers have proposed various techniques but many of them have neglected the iteration convergence rate for the solution. Optimal placement of distributed energy resources has to deal with constraints like size, location, number, power factor and type. Enhanced particle swarm optimisation and genetic algorithm technique for optimal penetration and sizing of renewable distributed energy resources in the IEEE 33 bus radial distribution network has been applied. Enhanced particle swarm optimisation and genetic algorithm techniques have been applied for power loss reduction, enhancing voltage profile and minimising the iteration for the convergence rate of the solution.

Qualitative and Quantitative Evaluation of Overcurrent Protection in Active Distribution Systems

Greenhouse gas emissions are considered a global problem, which is why interest in implementing renewable distributed energy resources (DER) is increasing worldwide. However, despite the contributions to the reduction of fossil primary energy sources, the connection of DERs to power grids often gives rise to new problems in control, operation and protection systems that need to be analyzed and solved. The objective of this paper was to compare a conventional overcurrent protection scheme and several adaptive schemes for active distribution networks (ADN). The proposed methodology consisted of performing an analysis of the adaptive protection approaches for ADN. From this evaluation, the most relevant documents were classified according to the main requirements, contributions, and conclusions. Additionally, some of these proposals were tested with conventional overcurrent protection, using the ATP simulator and Python software. Finally, a quantitative and qualitative analysis of the proposed protection approaches was performed in order to identify limitations to be considered in future research work. The results obtained, when considering different faults and modes of operation of the DNA, showed that the conventional protection coordination approach is highly vulnerable to faults when DERs are connected, mainly due to the change in current magnitudes and direction. The adaptive schemes analyzed have adequate performance, however, several application issues need to be analyzed in the future.

A coordinated cyberattack targeting load centers and renewable distributed energy resources for undervoltage/overvoltage in the most vulnerable regions of a modern distribution system

An integral step for system operators in protecting modern distribution systems and smart cities against future cyberattacks is to primarily scrutinize complex cyberattack models targeting smart grids from the viewpoint of attackers. This paper proposes a cyberattack framework to result in simultaneous undervoltage and overvoltage in a smart distribution system equipped with renewable energy resources. In the upper level of the framework, attackers separately solve an optimization problem to result in optimal and suboptimal candidates of false data injection attack vectors leading to possible scenarios of under/overvoltage in the system. In the lower level, the attackers solve the second optimization problem collaboratively to identify the attacks targeting the most vulnerable regions of the system to undervoltage and overvoltage at the optimal time. In case the optimal time of attackers is not the same, a trade-off platform is designed within the framework such that they can compromise and identify a reasonable time for such a simultaneous cyberattack.

Bi‐level planning of distributed energy resources into existing transmission grids: Pathway to sustainable energy systems

This paper presents a novel stochastic planning framework for the integration of renewable distributed energy resources (DERs) into existing power systems without relying on new investments in the transmission networks. The upper-level problem of the proposed model aims at minimizing the total expected social cost of supplying demand that includes the expected cost of getting energy from conventional generating units and DERs, the congestion cost of transmission networks, and the greenhouse gas (GHG) emission cost, while each of the privately invested DER satisfies a specified rate of return. The lower-level problem clears the electricity market to find locational marginal prices (LMPs) and operation status of the system. The proposed framework is formulated as a bi-level optimization problem that is recast as a single-level problem using the duality technique. The non-linear terms are then linearized using the “bigM” method and the complementary slackness conditions. The uncertainties in the power production of renewable resources and the future electric loads are captured using the scenario technique in which the copula method is employed for considering the correlation between uncertainties. Finally, the effectiveness and applicability of the proposed method are validated on a 3-bus test system and the modified IEEE RTS 24-bus system.

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems.

The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

Concepts, Configurations, and Challenges of Solid-State Transformer: A Review

Abstract: Rapid and suddenly increasing use of small distributed renewable energy resources (DRER) and distributed energy storage (DES), reduced compact dynamic electrical power consumer-like electrical vehicle loads, the requirement of bidirectional power flow communication network to exchange critical information, other ancillary services, etc. has to be envisaged and fulfilled by a future smart distribution grid system. A solid-state transformer (SST) has more potential solutions to provide stable and efficient distribution system operation to overcome the above problem.From various designs of converter control schemes, the solid-state transformer concept has been evaluated and summarised. The benefits and downsides of converters in the AC/DC, DC/DC, and DC/AC stages are studied for the best configuration. The High Frequency Transformer(HFT) is a main part of SST.The design and optimization of a HFT is approached on the objective of minimising the weight and size of the SST.Researchers also face several challanges in the prototype design and implementation of SST before operating effectively in the distribution system which is presented . Some of expected solutions and future recommendations for the establishment of the solid state transformer for future smart electrical distribution systems are discussed.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review

The electric industry is developing towards a more efficient, reliable, and resilient electric power network. In this way, utilizing distributed energy resources (DERs), especially renewable DERs (RDERs) are a paradigm change. DERs offer many advantages in power systems, including transmission loss reduction, environmental benefits of RDERs, and enhancement of security, reliability, and resiliency of the network. However, high penetration of DERs increases uncertainty and challenges the efficient and reliable operation of the power system. This paper provides a thorough review of the transactive distribution platform which is essential to address the aforementioned challenges. This distribution platform includes a transactive distribution system operator providing a seamless and coordinated control to dynamically balance supply and demand and follow uncertain generations of RDERs. Indeed, this review paper highlights the capability of transactive energy (TE) by considering its key aspect in providing energy sharing opportunities for the integration of DERs into smart grids (SGs). TE is acombination of economic and control methods that enable optimum, reliable, sustainable, and efficient operation of SGs. Furthermore, a fully transactive framework offers more choices for DERs to control and manage the energy transactions in the retail market, as well as improves the inter operability among various market players.

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

An Investment Efficiency Evaluation Model for Distribution Network With Distributed Renewable Energy Resources

An Investment Efficiency Evaluation Model for Distribution Network With Distributed Renewable Energy Resources