Dynamic Virtual Power Plants: A New Paradigm for Grid Services and Control

The increased grid integration of distributed generation technologies leads to significant structural changes for grid automation and control. There are still no sufficient strategies that are available for active grid integration of distributed generation units and their participation in grid control. New flexible concepts for control of the power system are required to ensure stable and secure operation. Combined with advanced control strategies, distributed generation technologies are effective potential to provide and support secured power supply. This paper introduces a multi-level hierarchical control strategy for efficient grid integration of distributed generation units and especially increases availability and reliability of future power systems. The investigations in this paper concentrate on control concepts of the frequency and the related active power, since the frequency is the ubiquitous grid state variable, representing the balancing of active power generation and consumption. It furthermore paves the way for advanced power system optimization and management functions like virtual power plants, etc.

Power grids are critical infrastructure in modern society, and there are well-established theories for the stability and control of traditional power grids under a centralized paradigm. Driven by environmental and sustainability concerns, power grids are undergoing an unprecedented transition, with much more flexibility as well as uncertainty brought by the growing penetration of renewable energy and power electronic devices. A new paradigm for stability and control is under development that uses graph-based, data-based, and distributed analysis tools. This article surveys classic and novel results on the stability and control of power grids to provide a perspective on this both old and new subject.

This lecture treats the problem of designing local control agents for cooperating components in a network of interacting dynamic systems. Each local control agent must ensure that all local specifications are met, but at the same time must ensure that the different components help each other in achieving good global behavior as well as good local behavior. This problem will be illustrated by using urban traffic control and smart electric power grids as examples. Centralized or hierarchical control approaches are not robust against failures in communication networks, and require unrealistic assumptions on the knowledge of each agent about the overall model. A completely decentralized approach, where each local control agent selfishly tries to achieve its local specifications only, runs a high risk of global interactions that may destabilize the system, making it impossible to achieve the specifications. This talk proposes two paradigms for distributed feedback control that require very little information exchange and very little global model knowledge. The leader/follower control paradigm is illustrated for urban traffic control: heavily loaded leader agents send messages to their follower neighbors requesting that these followers give green only to platoons of vehicles travelling towards the leader intersection at those times when this will be optimal for the performance of the leader. Another coordination paradigm is called the coordinating model predictive control (CMPC). Consider a power transmission network that has been partitioned in interacting regions, where CMPC is used in order to prevent the spread of the disturbances following incidents like line or machine failure. CMPC tries to resolve this by having each local control agent apply a model predictive controller, using as on-line available information not only the local voltage and current measurements, but also information on the planned sequence of future control actions of neighboring agents, communicated to it from time to time. This talk will discuss some of the minimal requirements for modeling, communication and control agent set-up in order to robustly achieve specifications using distributed control.

Aiming at the power balance control of multi-source energy storage grid in the case of a high proportion of new energy grid connection. In this article, a power grid dynamic partition method based on the Markov energy field principle and a priori knowledge model is proposed. Combined with the coordinated dispatching of power grid source-load, a two-layer power balance partition control model based on the topological characteristics of multi-source energy storage nodes is established. First, in the upper-layer model, the energy homogenization method of multi-source energy storage nodes is studied, and the Markov energy field model of power grid node partition based on energy interaction constraints between nodes is established to partition the power grid initially. Combined with the prior model of node dynamic partition, the initial partition is dynamically optimized to realize the dynamic partition of the multi-source energy storage grid. Then, in the lower-layer model, the source-load coordinated dispatching model in the power grid partition area is established to realize the dynamic partition control of the power grid. Finally, based on the real operation data of a northeast power grid and IEEE39 node system, a dynamic partition power control simulation model of a multi-source energy storage power grid is established. The simulation results and analysis show that the dynamic partition power control strategy proposed in this article can effectively improve the regulation ability and economy of the power grid.

The usage control model (UCON) is a new access control paradigm proposed by Park and Sandhu that encompasses and extends different existing models. Its main novelty, in addition to the unification view, is based on continuity of usage monitoring and mutability of attributes. We identified this model as a perfect one for managing access/usage control in GRID systems due to their peculiarities. Here we extend and systematize our previous work on usage control to develop a full model for usage control in GRID We use as policy specification language a process description language and show how this is suitable to model the usage policy models of the original UCON model.

To achieve the national carbon-peak and carbon-neutral strategic development goals, it is necessary to build power systems dominated by renewable and sustainable energy. The future power system with a high proportion of renewable and sustainable energy is required to have large-scale, low-cost, flexible, and adjustable resources. To this end, this article aggregates user-side distributed energy storage and electric vehicles into a virtual power plant, considering the uncertainty of wind power fluctuations and the uncertainty of electric vehicle charging and discharging to establish a day-ahead and intra-day peak regulation model for combined peak regulation of virtual and thermal power plants. The bounding algorithm seeks the optimal strategy for the two-stage model of joint peak regulation and obtains the day-ahead and intra-day two-stage optimal peak regulation strategy. The simulation example shows that the virtual power plant and its day-ahead and intra-day optimal peak regulation strategy can reduce the peak regulation cost of the power system, as compared with the deep peak regulation of thermal power plants with a special supporting energy storage power station. This work provides a global perspective for virtual power plants to participate in the formulation of power system peak regulation rules.

As distributed energy resources (DERs) play an increasing role in generation and grid services, control and market paradigms will need to be tested with models that incorporate transmission and distribution systems. The test systems will need to be realistic and accessible for researchers to form conclusions about realistic behaviors and maintain comparability and repeatability of results. These test systems also must have high resolution models of sufficient size for realistic distribution and transmission interaction. This paper demonstrates the tightly coupled HELICS-based co-simulation of realistic synthetic distribution and transmission models for the Austin metropolitan area and how this framework can be used to analyze resultant behaviors from distributed resources and their controls. These results are compared to the same distribution system run without co-simulation using a fixed feed-in voltage. Results show that even without advanced voltage controls, the feed-in voltage response when coupled to the transmission system results a lower level of voltage excursions, suggesting the importance of using this type of realistic, tightly coupled, transmission and distribution cosimulation when assessing DERs.

Recently, infiltration of distributed energy resources (DERs) is augmented considerably to upsurge network flexibility, better economic indicator, and reduced power loss. But integration of different DERs may cause challenges in power grid. To overwhelmed these challenges and obtain maximum advantage of DERs, virtual power plant's concept has been emerged. Virtual power plants (VPPs) has the capacity to partake in electricity market and rivalry of VPPs to achieve more profit, deregulated multi‐operator markets are developed. This paper suggests dynamic optimum power flow (DOPF) for multi‐operator VPPs considering demand side management (DSM) and uncertainty of renewable energy sources. VPPs with different proprietorships are interconnected with each other by tie lines. Each VPP has small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plant (BPPs), and plug‐in electric vehicles (PEVs). VPP 1 comprises IEEE 33‐bus system, VPP 2 comprises 15‐bus system, and VPP 3 comprises IEEE 69‐bus system. Bottlenose dolphin optimizer (BDO), HPSO‐TVAC, and GWO have been applied to solve DOPF problem and maximize the net profit of multi‐operator VPPs.

The present paper presents a Cyber Physical Power System (CPPS) framework based on the Service-Oriented Architecture for proactive transmission grids control, modeling and monitoring. CPPS paradigm aims at integrating and coordinating computation, networking, and physical processes according to a holistic vision of the transmission system. The key feature of the proposed framework is the ability of multiple entities to process, manage and share massive heterogeneous information. More specifically, the idea is to conceptualize a holistic architecture that enables the computing resources to deliver much more automation that the sum of its individually self-managed components, allowing the Transmission System Operator to improve the interoperability and the integration level of monolithic and hard to customize power system control and monitoring functions. Moreover, it allows TSO to develop content-based data extraction and aggregation from a host of pervasive sensors network and to exploit distributed embedded computing resources aimed at solving large-scale problems. To assess the benefits of the CPPS framework, the first experimental results obtained on a real test bed are presented and discussed.

This paper discusses a new paradigm of network service and in-network resource control: relational metric based control. The relational metrics indicate the closeness relationship between objects in the real world, and these objects could be people, locations, things, and content. Closeness is measured by using a fusion of online and physical sensing. We will describe the system model and discuss possible service applications of this technology.

Guest Editorial: Situational awareness of integrated energy systems

Power industry is facing revolutionary changes. The direction of the US Government to low carbon footprint and, as a consequence, high penetration of renewable energy resources and smart grid technologies are completely transforming planning and operational patterns for electric grid. As more and more variable and demand response resources being integrated into the electric grid, the grid operation is experiencing increasing level of uncertainties. The decision-making process under such environment becomes more challenging. The grid architecture and control also become more and more decentralized requiring new control paradigms and reliability metrics to be investigated in order to achieve much higher level of flexibility and resilience. These changes are disruptive enough to cause even transformations in utility business dealing with completely unknown situations. On the other hand, the evolution in computing; generation, transmission, and distribution technologies; and mathematical methods creates opportunities for innovation in power system design and control. New mathematical models for power system analysis and operation are being developed to address above challenges. We will discuss the need for new power system control and electricity market design directions while managing grid complexity.

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.

Smart grid technology promises to prepare today's power systems for the challenges of the future by extensive integration of information and communication technology (ICT). One key aspect is the control paradigm which will have to be shifted from completely centralized control systems to more dezentralized concepts in order to adapt to the distributed nature of smart grids. Multi-agent systems (MAS) are a very promising approach for designing distributed, decentralized systems, naturally also in the field of smart grids. This work introduces the notion of decentralized multi-agent-based control systems (DMACS) and aims to give an overview on the different requirements and challenges on the way from current centralized control systems to DMACS. Therefore, different ICT scenarios and MAS topologies are employed to discuss the decentralization of three exemplary smart grid applications: voltage/var control, virtual power plants, and dynamic islanding. As a result, the advantages and challenges as well as ICT requirements of agent-based decentralization are outlined.

This article describes problems related to the operation of a virtual micro power plant at the Faculty of Electrical Engineering (FEE), Czestochowa University of Technology (CUT). In the era of dynamic development of renewable energy sources, it is necessary to create alternative electricity management systems for existing power systems, including power transmission and distribution systems. Virtual power plants (VPPs) are such an alternative. So far, there has been no unified standard for a VPP operation. The article presents components that make up the VPP at the FEE and describes their physical and logical structure. The presented solution is a combination of several units operating in the internal power grid of the FEE, i.e., wind turbines, energy storage (ES), photovoltaic panels (PV) and car charging stations. Their operation is coordinated by a common control system. One of the research goals described in the article is to optimize the operation of these components to minimize consumption of the electric energy from the external supply network. An analysis of data from the VPP management system was carried out to create mathematical models for prediction of the consumed power and the power produced by the PVs. These models allowed us to achieve the assumed objective. The article also presents the VPP data processing results in terms of detecting outliers and missing values. In addition to the issues discussed above, the authors also proposed to apply the Prophet model for short-term forecasting of the PV farm electricity production. It is a statistical model that has so far been used for social and business research. The authors implemented it effectively for technical analysis purposes. It was shown that the results of the PV energy production forecasting using the Prophet model are acceptable despite occurrences of missing data in the investigated time series.