Renewable Distributed Energy Resources Research Articles

Graph‐based solution for smart grid real‐time operation and control

AbstractUnder the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed.

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm’s performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results.

Price-Responsive Prosumers in Transmission-Constrained Power Markets: Pricing, Investment, and Social Welfare

The emergence of prosumers with renewable distributed energy resources has led to a paradigm shift in the electricity market. We formulate a complementarity problem to analyze a transmission-constrained power market in the presence of price-responsive prosumers who invest in photovoltaics (PVs) coupled with battery storage under two pricing schemes: net metering and net billing. Our study has three main findings. First, both investments in PVs and transmission tariffs are greater under net metering than under net billing over the range of PV capital costs. Second, the prosumer investments in PVs increase when coupled with battery systems under both pricing schemes. Third, the total social surplus under net billing tends to be larger than that under net metering. Moreover, the gap in the total social surplus between the pricing schemes widens when battery capacities are larger. Overall, we find that net billing could be a more efficient regulatory scheme, possibly avoiding over-investment in PVs.

Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach

This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

A robust microgrid using an inverter with CCS-MPC control and resilient operation

Modern generation trend through renewable energy-based distributed energy resources has made microgrids an important part of the power system. Robust control of the inverter providing quality power has become an essential microgrid (MG) requirement. This paper proposes continuous control set model predictive control (CCS-MPC) on inverter for dynamic voltage regulation supplying reactive power to the load for a MG. The proposed method uses the terminal voltage magnitude, which reduces calculation time, sensor per phase, and the engagement burden of the controller. This paper applies a nonlinear VSI model and a series parallel structure nonlinear autoregressive exogenous (NARX) model of artificial neural network (ANN) to predict future output which CCS-MPC optimizes. The proposed VSI with CCS MPC is applied to a case study of a real system as a plug-and-play device. Simulation shows the satisfactory result of the proposed controller. The inverter control system has been made resilient to protect itself and automatically recover from faults on the DC and AC side, providing quality power to its local load when the fault has been removed.

Decentralized Control for Multiple Network-Forming Inverters in a Solar PV-BESS Based Standalone Microgrid System

—This research paper focuses on decentralized control of an AC microgrid in standalone mode. The microgrid includes three solar PV arrays accompanied by three battery energy storage systems (BESS), utilizing renewable distributed energy resources (DERs). The connection between each DER system and the microgrid is established through a network-forming three-phase voltage source inverter (VSI). The microgrid’s operation is regulated using modified droop control, incorporating virtual impedance and feed-forward derivative in primary control, along with a phase-locked loop (PLL) system for inverter synchronization. A master-slave control configuration is employed between the VSIs to mitigate voltage amplitude and frequency deviations induced by primary control. These enhancements ensure smooth power distribution and maintain voltage amplitude and frequency within specified limits across different operational scenarios. Control operations are performed in a stationary frame ( frame) using Clarke’s transformation, and PR controllers regulate VSI’s output voltage and current. The research paper shows the effectiveness of controlled microgrid operations using MATLAB/Simulink software in various conditions including intermittent PV system behavior, load dynamics, and DER connection/disconnection challenges. Simulation results show that the proposed approach enhances stability and performance of the microgrid system, especially in managing power fluctuations and maintaining a constant frequency response.

An Auto-Tuned Robust Dispatch Strategy for Virtual Power Plants to Provide Multi-Stage Real-Time Balancing Service

To fully exploit the flexible potential of distributed energy resources (DERs) in providing balancing service to the power system, Virtual Power Plants (VPPs) act as control centers to conduct the optimal real-time dispatch of their managed DERs. This study investigates a VPP’s auto-tuned robust policy based on a multi-stage distributionally robust optimization model (DRO) in response to the uncertainties from both the setpoint of the top-level system operator (SO) and the outputs of renewable DERs. We propose a concise paradigm to reduce the complexity of the original large-scale optimization task. Specifically, we first cast the multi-stage DRO problem into a dynamic programming (DP) formulation and further simplify it to derive a single-stage convex optimization control policy (COCP) at each time stage. Further, an automatic update method based on implicit differentiation is employed to tune the parameters of COCP. Case studies show that this method ensures higher solution quality and faster convergence during training than conventional tuning methods. The proposed COCP outperforms other stochastic optimization techniques in terms of robustness, efficiency, and computational speed.

Voltage Stability Assessment of a Campus DC Microgrid Implemented in Korea as a Blockchain-Based Power Transaction Testbed

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM.

Uncertainty-Aware Trading of Congestion and Imbalance Mitigation Services for Multi-DSO Local Flexibility Markets

The design of Local Flexibility Markets (LFMs) for energy and reserve dispatch of Renewable Distributed Energy Resources (RDERs) has recently been a topic of wide research. However, in an scenario with high penetration of RDERs deployed among different Distribution System Operators (DSOs) jurisdictions, further operational requirements concerning congestion and imbalance mitigation services may lay down. In this context, the relationship between capacity and energy products and the uncertainty management scheme becomes essential for procuring of RDERs flexibility. This paper proposes an uncertainty-aware Multi-DSO LFM. This market setting uses flexibility products to mitigate congestions and imbalances among different DSOs. First, capacity products hold back the flexibility of the RDERs in anticipation of contingencies. Then, energy products are activated within each time slot if the event finally occurs. LFM is solved in a coordinated and decentralised fashion using the properties of the Alternating Direction Method of Multipliers (ADMM), preserving participants' privacy. Uncertainty of RDERs and energy events duration are modelled using chance-constraint linear optimisation. The proposed methodology has been tested in a case study based on a realistic dataset and radial distribution systems.

Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets

The regional integrated energy system (IES) is considered a promising paradigm for economic multi-energy provision while promoting the accommodation of renewable distributed energy resources (RDER). However, various uncertainties faced by the IES pose challenges to fulfilling its functionalities and economic promises. This paper proposes a day-ahead optimal bidding strategy of IES in electricity markets with multiple time scales of electricity settlement and energy dispatch for minimizing its expected operational cost based on the hybrid scenario-based stochastic and chance-constrained programming. It enables the IES to determine the optimal bidding electricity quantities in the day-ahead market in a multi-energy complementary manner by incorporating the potential real-time clearing price scenarios, with the uncertainties introduced by RDER generation and multi-energy loads efficiently addressed using chance constraints. Herein, a gradient boosted regression tree (GBRT) based quantile forecasting method is developed to equivalently reformulate the chance constraints to tractable deterministic constraints without any prior knowledge or probability distribution assumptions. The proposed day-ahead bidding strategy is extensively assessed through simulation experiments in the PJM and Guangdong electricity markets and the numerical results confirm the effectiveness of the proposed solution.

Supply–demand scenario generation of active distribution systems using conditional generative adversarial networks

The intermittency of distributed renewable energy resources (DRERs) and the fluctuation of load demand make the supply–demand scenario of active distribution systems significantly different from that of traditional radial distribution systems. Scenario generation is an effective tool for describing supply–demand uncertainty and is therefore of great significance for the planning and operation of active distribution systems with a high penetration of DRERs. This paper proposes a supply–demand scenario generation method for active distribution systems using conditional generation adversarial networks (CGANs). The proposed method is data-driven and utilizes neural networks to learn and represent distribution laws from data without revealing the expression probability distribution. Specifically, it first extracts the features information of historical scenarios as the conditional control vectors (CCVs), then embeds the CCVs to the input of the generator network and discriminator network to achieve conditional control. The proposed approach is verified by the actual time series wind power data and load profiles. Results show that the proposed method can efficiently generate supply–demand scenarios satisfying various statistical characteristics and capture complex spatial–temporal correlations.

Automatic Recognition of Beam Attachment for Massive MIMO System in Densely Distributed Renewable Energy Resources

Several large-scale and distributed systems such as renewable energy systems (RESs) require ubiquitous and reliable communication. RESs are designed to provide efficient power management and improve both energy production and consumption. Decision making in RESs heavily depends on real-time communication. Fifth and sixth-generation (5G, 6G) wireless networks promise to deliver significant communication advantages to RESs including ultra-low latency, high throughput and improved coverage. However, the communication behavior in RESs is categorized as unpredictable due to aspects such as system flexibility and equipment heterogeneity. This may affect the stability of the entire RES, which results in further issues such as signal reliability and degraded coverage. Therefore, precise identification of user equipment’s (UE) location greatly improves the sustainability of 5G and 6G wireless services. In this work, we propose a novel low-complexity technique to automatically recognize UE locations in an area of interest. The approach aims at providing precise identification of UE with minimum memory and feature requirements. We use the lazy learning approach to build a prediction model to construct beam-attachment maps. We then train the model to provide distributed intelligent models to automatically recognize beam-attachment indexes. We compare the proposed approach with instance-based techniques to measure its ability at predicting beam-attachment maps. The results show that the proposed model has the ability to provide an accurate prediction with respect to the beam-attachment index (around 100%) with minimal complexity.

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.