Power Set Point Research Articles

Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.

Automating Component-Level Stress Measurements for Inverter Reliability Estimation

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Integration of air-cooled multi-stack polymer electrolyte fuel cell systems into renewable microgrids

Currently, there is a growing interest in increasing the power range of air-cooled fuel cells (ACFCs), as they are cheaper, easier to use and maintain than water-cooled fuel cells (WCFCs). However, air-cooled stacks are only available up to medium power (<10 kW). Therefore, a good solution may be the development of ACFCs consisting of several stacks until the required power output is reached. This is the concept of air-cooled multi-stack fuel cell (AC-MSFC). The objective of this work is to develop a turnkey solution for the integration of AC-MSFCs in renewable microgrids, specifically those with high-voltage DC (HVDC) bus. This is challenging because the AC-MSFCs must operate in the microgrid as a single ACFC with adjustable power, depending on the number of stacks in operation. To achieve this, the necessary power converter (ACFCs operate at low voltages, so high conversion rates are required) and control loops must be developed. Unlike most designs in the literature, the proposed solution is compact, forming a system (AC-MSFCS) with a single input (hydrogen) and a single output (high voltage regulated power or voltage) that can be easily integrated into any microgrid and easily scalable depending on the power required. The developed AC-MSFCS integrates stacks, balance of plant, data acquisition and instrumentation, power converters and local controllers. In addition, a virtual instrument (VI) has been developed which, connected to the energy management system (EMS) of the microgrid, allows monitoring of the entire AC-MSFCS (operating temperature, purging, cell voltage monitoring for degradation evaluation, stacks operating point control and alarm and event management), as well as serving as a user interface. This allows the EMS to know the degradation of each stack and to carry out energy distribution strategies or specific maintenance actions, which improves efficiency, lifespan and, of course, saves costs. The experimental results have been excellent in terms of the correct operation of the developed AC-MSFCS. Likewise, the accumulated degradation of the stacks was quantified, showing cells with a degradation of >80%. The excellent electrical and thermal performance of the developed power converter was also validated, which allowed the correct and efficient supply of regulated power (average efficiency above 90%) to the HVDC bus, according to the power setpoint defined by the EMS of the microgrid.

Energy Management and Control of Photovoltaic and Storage Systems in Active Distribution Grids

The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach.

Power Set-Point Tracking of a Wave Energy Converter With Multiple Power Take-Off Units in Irregular Waves

Ocean wave energy has been regarded as a promising and sustainable power source for offshore power system. In order to handle the uncertainty and fluctuation of the wave power, the power system is generally designed to be highly dependent on short-time energy storage facilities such as battery packs. This means the cost of energy storage facility tends to dramatically increase when a large wave farm is deployed. This paper proposes a model predictive control algorithm to realise the continuous power set-point tracking of a single six-of-freedom point absorber with multiple power take-off units, to mitigate the dependence of the energy storage in offshore power supply. The key controller settings that determine the power quality of the wave energy converter are analysed over various power set-points. Furthermore, the performance of power set-point tracking is assessed over significant wave heights and peak periods. It is found that although the power set-point tracking algorithm only achieves 10-27% of power generation capacity at the expense of reducing power fluctuation, the controlled point absorber is still capable to serve as a high-quality power source in some of offshore scenarios.

Cooperative Markov Decision Process for Voltage Control of Grid-Tied Photovoltaic Generators With Reactive Power Support

The problem of voltage control (VC) by means of inverter reactive support in distribution grids with photovoltaic (PV) generation is addressed. A novel decentralized approach for VC is proposed, which consists in heuristic specification of a distributed reactive support cooperative partially observable Markov decision process (DRS-C-POMDP) whose agents control groups of PV nodes clustered according to voltage correlation. The proposed method employs offline policy optimization, which yields closed-form reactive power setpoint control laws. Hence, the online processing time required by most VC methods is avoided and controller implementation is simplified. The method is validated via simulation on a 123-bus feeder, in which it is compared to other decentralized heuristic methods from the literature. The results indicate that, for identical local inverter controls, the proposed method yields superior performance.

Data-driven adjustable robust solution to voltage-regulation problem in PV-rich distribution systems

Adjustable robust counterpart (ARC) methodology has been recently utilised in distribution system operation to allow fast-acting devices, such as PV inverters, to take live recourse actions in response to operating point deviations. Available ARC models are used to obtain the controller parameters based on the worst-case realisations. However, we show that such a strategy might lead to over-conservative results when uncertainty realises away from the worst-case. To close this gap, this paper extends ARC to allow exploiting probabilistic information of uncertainty within the optimisation problem. This results in controller parameters that not only secure the worst-case but also work better in expectation. We formulate our approach as a linear program that minimises the real power curtailment (RPC) and reactive power usage of PV inverters while maintaining network voltages in an acceptable range. In contrast to the conventional ARC, our approach considers a segmented uncertainty set, and assigns a probability to each segment based on historical data. It then optimises to find inverter controller parameters in the form of piecewise affine functions, with each piece associated with one segment in the segmented uncertainty set. In live operation, the controllers use the real-time local measurements and assigned piecewise affine functions to generate real and reactive power set-points for the inverters. The performance of the proposed approach is validated using Monte-Carlo simulations on IEEE 37-bus and 906-bus networks. The simulations show that the proposed approach improves the performance of ARC by 30% to 60% over a variety of experiments.

A hybrid energy storage solution based on supercapacitors and batteries for the grid integration of utility scale photovoltaic plants

This paper presents a 2-level controller managing a hybrid energy storage solution (HESS) for the grid integration of photovoltaic (PV) plants in distribution grids. The HESS is based on the interconnection of a lead-acid battery pack and a supercapacitor pack through a modular power electronics cabinet. The inclusion of the HESS into the PV plant –and not an state-of-the-art energy storage system based on a single technology–, is motivated by the diversity of technical requirements for the provision of the services of grid peak power shaving and PV output power ramp limitation. The 2-level controller ensures a synergistic exploitation of the two storage technologies aiming for an optimal service level of the HESS and minimum battery degradation. The higher level of the controller is based on a mathematical optimization problem that solves with the optimal schedule of the storage technologies for peak power shaving purposes. The power setpoints of this optimization are then complemented by a real time controller managing PV plant output ramp limitation. The HESS performance and associated controller has been proved effective through two case studies. The first one adopts a 6.6 MW PV plant including a HESS solution combining a 5.5 MWh and 2.64 MW lead-acid battery pack with a 0.25 MWh and 1.32 MW supercapacitor pack. The second one reports experimental data from an analogous scenario scaled down to kW level and using a laboratory scale prototype for the HESS. All in all, the hardware and software solutions proposed in this paper contribute to a feasible exploitation of multi-purpose energy storages targeting the needs of renewables' and distribution system operators.

Energy management of stationary hybrid battery energy storage systems using the example of a real-world 5 MW hybrid battery storage project in Germany

Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important for the transition to large shares of renewable energy sources in the electricity grid. Hybrid battery storage systems are an interesting option to increase the profitability of BESS by combining low-cost battery technologies with more expensive, but also more efficient and robust ones. In theory, such a hybrid system can be cheaper than a single-technology system by leveraging synergy effects while still satisfying the requirements of a given application. An energy management system, which allocates the setpoint power of the BESS to the individual battery technologies, is crucial for taking advantage of the hybrid system layout. This paper details an analysis of different energy management algorithms for hybrid BESS using the example of a real-world project called M5BAT and compares the latter to alternative BESS layouts. Besides two heuristic algorithms, a non-predictive optimization and a predictive optimization are developed. Models comprising the electrical, thermal and aging behavior of the considered BESS components are introduced. Simulations of the operation of the BESS providing frequency containment reserve are conducted for determining the operating costs. The performance of the algorithms is evaluated based on the simulation results. Comparing the results shows significant differences in the operating costs between the algorithms, especially when optimized to reduce operating costs. The economic advantage of hybrid BESS is validated by additional simulations of a virtual hybrid BESS and a virtual single-technology BESS. Although the layout has not been optimized in terms of individual sizing of the different battery technologies, the hybrid BESS show a considerable advantage over the single-technology BESS.Highlights-Comparative study of energy management algorithms for power allocation in hybrid battery energy storage systems.-Development of heuristic power allocation algorithms and optimization approaches for cost-optimal operation.-Modeling of all relevant components and effects that contribute to the operating costs.-Simulations based on frequency-containment reserve operation of a real-world system.-Evaluation of the advantage of hybrid over mono-technology battery energy storage.

Multi-Objective Hierarchically-Coordinated Volt/Var Control for Active Distribution Networks With Droop-Controlled PV Inverters

Due to increasing installation of photovoltaic (PV) units, reactive power compensation from PV inverters contributes significantly to Volt/Var control (VVC) for active distribution networks. While PV inverters support VVC functions, lack of systematic coordination and heavily varying PV power generation lead to low control efficiency. To maximize benefits of the inverter-based VVC, this paper proposes a multi-objective hierarchically-coordinated VVC method with droop-controlled PV inverters. This method aims to minimize both average bus voltage deviation and network power loss, by simultaneously optimizing PV inverter reactive power setpoints for central control and droop control functions for local control. The droop control characteristics of PV inverters are fully modeled, including voltage ranges of the dead band and control zones, as well as droop slope gradients. In addition, this paper applies a Taguchi’s orthogonal array testing technique to handle random variations of PV power generation in the optimization problem. Moreover, to efficiently solve this optimization problem with integer variables, this paper proposes a solution algorithm based on model relaxation and a feasibility pump method. The proposed method is tested on two distribution systems, and simulation results verify highly efficient control performance in comparison with existing methods.

Frequency regulation and congestion management by Virtual Storage Plants

The potential role of energy storage as flexible resource to help meet the challenging energy and environmental targets is well recognized. It can provide both steady-state and dynamic support to the power networks. A Virtual Storage Plant (VSP) is an aggregation of heterogeneous energy storage units that act as a single storage device to provide system-level support. This paper introduces an innovative and scalable hierarchical control framework for the optimal coordination of a large number of distributed storage devices through VSPs to provide frequency regulation and congestion management in multi-area power networks. The proposed approach combines distributed optimization and consensus-based control. At a lower level, a consensus-based control is designed to track power setpoints and to maintain a balanced state-of-charge among storage units. This facilitates the modeling of VSPs at higher level, where a distributed optimization approach is applied to achieve cross-regional coordination of multiple VSPs to deliver frequency support to interconnected power systems, while avoiding congested power flows over the tie lines. The proposed control framework is assessed on a realistic case study implemented by using MATLAB and the high-fidelity power system simulator DigSILENT. The IEEE 39 Bus system and a real regional power network in South-East Europe are considered in the case study. The simulation results demonstrate the promising performance of the proposed approach and the effectiveness of VSP in promoting large-scale storage integration.

Quantitative Assessment of Flexibility at the TSO/DSO Interface Subject to the Distribution Grid Limitations

In the last years, renewable energy sources have been changing the power system by making it more challenging to balance the generation and demand at every single point in time. The increasing penetration of distributed generation represents another trend at the distribution level that impacts the exploitation of existing distribution assets. In this context, the flexibility of distributed energy resources connected to the distribution systems may play an important role. The flexibility products are represented by variations in the scheduled/expected active and reactive power setpoints. Recently, regulatory bodies suggested many proposals and undertook actions for enabling new players, such as the distributed energy resources connected to the distribution systems, to provide both system and local services. However, currently, there are still barriers that might limit their effective involvement. Market schemes have been proposed for opening the participation of distributed energy resources in the service markets. This paper proposes an analytical quantification of how much the use of flexibility by the transmission system operator can influence the distribution system operator activities and the expected costs. The final goal is quantifying the flexibility that the transmission system operator can procure from the distribution system without a harmful impact on the distribution network operation. The paper investigates the expected interactions between the use of flexibility for power system balancing and security and the operation of distribution systems. The application of the methodology to a significant Case Study showed that even though the fit and forget approach causes a hypertrophic development of distribution systems to host distributed generation, the transmission system operator cannot obtain the required flexibility services or has to pay extra costs for bottlenecks caused by distribution system operational issues.

Bidding Strategy for VPP and Economic Feasibility Study of the Optimal Sizing of Storage Systems to Face the Uncertainty of Solar Generation Modelled with IGDT

Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).

Failure-Tolerant Fully-Distributed OPF Algorithm for System-Level Control of Multi-Terminal DC Grids

In the future multi-terminal DC (MTDC) distribution grids, with high penetration of converter-interfaced distributed energy resources (DER), the system-level control is expected to be realised through a fully-distributed optimal power flow (OPF) algorithm that determines the voltage and power set-points for the converters. The OPF problem is divided to nodal OPF sub-problems, which are solved by distributed control units (DCUs) operating as a networked control system (NCS). This paper presents a fully-distributed OPF algorithm with an incorporated strategy, to survive failures in the NCS, such as permanent changes in the NCS topology due to the crash of a DCU or the loss of a communication link. By changing the constraints of the OPF sub-problems of the affected DCUs upon the detection of the failure in the NCS, the fully-distributed algorithm becomes tolerant to permanent changes in the NCS topology and converge to a feasible OPF solution. Simulation results in different IEEE networks and failure scenarios demonstrate the fast convergence, irrespective of the number and location of the failures in the NCS or the failure time-point during the execution of the algorithm.

Modeling of Photovoltaic Inverter Losses for Reactive Power Provision

In addition to their main functionality of converting DC input power to AC output power, today’s photovoltaic inverters are generally required to be capable of providing reactive power. While there are well-established mathematical models that use the correlation between inverter losses and the transmitted active power to estimate inverter efficiency for any given active power operating point, the additional losses in the inverter due to the supply of reactive power are less well studied. In this work, the conversion efficiencies of three different photovoltaic inverters were measured for various active power and reactive power setpoints. Based on these measurements, two mathematical models are proposed to represent the conversion losses as a function of active and reactive output power. One model is of empirical nature and expands preexisting models to include terms that take the reactive power into consideration. The other model takes the dominant loss mechanisms in the conversion stage of inverters into account and considers the effect of reactive power provision on them. Both models were compared with a model variant proposed in literature. They are shown to perform with high accuracy over the entire operating range, while requiring only a small number of known efficiency values for parametrization. There are several fields of application for these new models: They allow photovoltaic park operators to precisely estimate the individual losses of a solar power plant that feeds reactive power into the grid. For grid operators, they enable a comparative loss analysis for different reactive power sources.

LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management.

Safe Data-Driven Secondary Control of Distributed Energy Resources

In this paper, we present a data-driven secondary controller for regulating to some desired values several state variables of interest in an inverter-based power system, namely, electrical frequency, voltage magnitudes at critical buses, and active power flows through critical lines. The secondary controller is based on online feedback optimization, leveraging the learned sensitivities of changes in the state variables to changes in inverter active and reactive power setpoints. To learn the sensitivities accurately from data, the feedback optimization has a built-in mechanism for keeping the secondary control inputs persistently exciting without degrading its performance or compromising system operational reliability. To ensure safe and reliable operation, we present an approach based on Gaussian process regression that, by making an inference about the modeling uncertainties not accounted for in the sensitivity-based prediction model, allows the controller to correct the predictions and find safe control actions, for which the prediction errors are more likely to be small. The feedback optimization also utilizes the learned power-voltage characteristics of photovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow the PV arrays to track the power setpoints. To learn the power-voltage characteristics, we separately execute a data-driven approach that fits a concave polynomial to the collected power-voltage measurements by solving a sum-of-squares (SoS) optimization. We showcase the secondary controller using the modified IEEE-14 bus test system, in which conventional energy sources are replaced with inverter-interfaced DERs.

Distributed Voltage Control for Three-Phase Unbalanced Distribution Systems With DERs and Practical Constraints

Traditional voltage control schemes in the power distribution system are typically centralized or local. Local control often gives nonoptimal solutions utilizing local measurements and lacks global coordination. Centralized control gives optimal solutions but lack scalability and robustness, especially with the increasing number of distributed energy resources (DERs). Recent advancement in power electronics with improved automation and digitalization in power grid control provides an opportunity to enable distributed optimization approaches for voltage control. This article proposes an optimal distributed voltage control algorithm based on augmented Lagrangian multiplier theory and primal-dual gradients for three-phase unbalanced distribution systems. In the developed algorithm, each node uses its own local measurements and neighboring nodes measurements to compute optimal reactive power setpoints, while meeting reactive power injection constraints for DERs. The proposed algorithm also considers practical constraints, such as communication delays, measurement noises, modeling errors, limited control devices, and infrequent switching required for field implementation. The proposed algorithm is validated for voltage control in an unbalanced, three-phase IEEE 123-bus feeder modeled in OpenDSS to demonstrate superior performance.

Coordinating Distributed Energy Resources and Utility-Scale Battery Energy Storage System for Power Flexibility Provision Under Uncertainty

Relying on the power flexibility of distributed energy resources (DERs) located in an active distribution network (ADN), this ADN will be able to provide power flexibility to the upper-layer grid at their point of common coupling (PCC). The power flexibility is defined as additional bi-directional active/reactive powers a resource can provide to the grid by adjusting its operating point. In this context, this paper presents a two-stage ADN management method to deliver, at the PCC, the power flexibility that the upper-layer grid operator would request minutes-ahead real-time operation. The first stage updates the power set-points of DERs considering their offer curves as well as the uncertainties stem from the short-term forecast errors of demand and renewable generation profiles. The inter-temporal constraints and losses of the grid are accounted for by exploiting a linearized dynamic optimal power flow model, whereby the first stage is implemented as a linear scenario-based optimization problem. Then, in real-time operation, relying on a linear optimization problem, the second stage adjusts the power flexibility injection of a utility-scale battery energy storage system (ESS) to mitigate the imbalance at the PCC inherent in the above-mentioned uncertainties. The performance of the proposed method is tested in the case of a real ADN located in the city of Aigle in southwest of Switzerland.

Modulation and Feed-Forward Based Damping Matching Strategy for Linear Generators

This article presents an active damping matching control strategy to optimize the output power of linear generators in energy harvesting applications. The method adjusts the magnitude of the current command responsible for active power set-point. The proposed approach combines a simple model-based feed-forward term with a slow but accurate correction based on the response of output power to a low-frequency modulation in set-point current magnitude. The correction is derived from the phase of output power oscillations and used to adjust the power set-point to achieve active-load matching conditions. In order to remove the low-frequency oscillations in the power at steady state, the method automatically stops the current amplitude modulation when maximum output power is reached. In addition, the proposed control incorporates an overstroke protection, which increases the active current component thereby reducing the stroke rapidly to a safe limit. The overall control principle is validated experimentally.