HVDC Transmission for Wind Energy Systems in Microgrids and Virtual Power Plants

The virtual power plant consisting of a large-scale energy storage system and a controllable energy source can reduce the potential safety hazards caused by the unstable output power of new energy when it is connected to the grid, thereby increasing the reliability of power supply. The energy storage system cooperates with the distributed photovoltaic and gas turbine to ensure that the virtual power plant participates in the system demand response, reduces the cost of power generation, and obtains more revenue. To this end, a virtual power plant operation strategy was designed. With the objective of maximizing the net profit of the virtual power plant in each period, a virtual power plant economic optimization configuration model was constructed, and a single-target particle swarm optimization algorithm was used to obtain the output of the energy storage system in the virtual power plant, and the signals are distributed to supercapacitors, lithium titanate batteries, and all-vanadium redox batteries through Fourier transform to realize the configuration of the energy storage system capacity and power.

In this chapter, the problem of microgrid (MG) and virtual power plant (VPP) concepts are considered as the most promising solutions to integrate distributed generation resources into the electric power system. Currently, the main drawback of distributed energy resource (DER) is that many of them are operating individually without any coordination with other electrical power sources, which leads to decrease of main grid reliability. MG, as a group of DER and interconnected loads, can connect and disconnect from the grid to enable it to operate in both grid-connected or standalone/isolated mode. With emerging of advanced communication and information technology, the small DER units would be aggregated then monitored, controlled and treated as a single large power plant. Bidirectional power flows, stability issues, uncertainty are the challenges to MGs. These can be overcome by integrating MGs as VPP. This concept can strengthen and diversified generation structure through decentralized dispatching and distribution energy networks. Additionally, a DER prosumers grouped together, controlled and coordinated by VPP may become significant part of the wholesale market. There are many approaches to the VPP concept. In this chapter, the concept of VPP for DER integration with power system is introduced and discussed. The focus will be paid to the modelling of proposed concept by MATLAB® SimulinkTM software and the results of the simulation in different operation scenarios have been presented.

This paper presents ancillary voltage control for a distribution feeder by using energy storage system (ESS) in microgrid. The microgrid consists of photovoltaic, wind power, and distribution generator. The active and reactive power control, voltage and frequency droop control for the power converter of ESS are proposed to reduce microgrid impact on the distribution feeder. The voltage variation along the feeder due to change of renewable power generation is derived by executing power flow analysis. The actual voltage fluctuation on the microgrid is measured, which is used to control active and reactive power output of the power converter via energy management system in the microgrid SCADA platform. Three operation scenarios of the microgrid with ESS are tested to verify the effectiveness of the proposed control method. It is found that the power output of ESS can be well regulated to reduce voltage fluctuation on the microgid, which is complied with grid connect code to achieve better voltage control performance for the distribution feeder.

With the increasing penetration of clean energy sources such as wind power and photovoltaic in the grid, the volatility, intermittency, and randomness of their power output have impacted the safe and stable operation of the grid. To address the challenges posed by scheduling and the potential wastage of renewable energy due to these factors, a two-layer optimal scheduling model for a virtual power plant that takes into account source-load synergy is proposed in this paper. In the upper model, emphasis is placed on demand response strategies to optimize load-side dispatch. This includes encouraging customers to adjust their electricity consumption patterns through time-of-use pricing and effectively managing controllable loads for peak shaving and valley filling. These actions collectively aim to maximize the virtual power plant's overall performance. The upper-tier model then communicates the power output to the lower-tier model. In the lower model, we consider the costs associated with wind, photovoltaic, thermal, and energy storage power generation to optimize power-side scheduling. This approach ensures a comprehensive optimization process, addressing both demand and power generation aspects of the virtual power plant's operations. Compared to existing research, this paper takes into account the impact of various clean energy sources, meticulously categorizes controllable loads, and conducts in-depth modeling and analysis of demand response. Finally, the results of optimal scheduling of the virtual power plant under different scenes are compared and analyzed using examples, to validate the effectiveness of the proposed method.

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.

A virtual power plant (VPP) is formulated and developed as a service-centric aggregator that enables the market integration of distributed energy resources and simultaneously supports cooperation with the distribution system operator (DSO) in addressing the issue of network usage. A suitable schedule of interactions and communications between aggregators, market operators, system operators, generators, and consumers, regarding electricity market participation and network operation is proposed and presented in a sequence diagram. The cooperation on congestion management in the distribution network is highlighted as solution to relieve network constraints via the optimal adjustment of active and reactive power of VPP resources while maximizing renewable energy integration across the pool under management. The VPP reduces uncertainty affiliated with input data by employing the latest forecasts through a rolling horizon approach in the planning stage. Thanks to the flexibility of the VPP to perform rescheduling in accordance with agreements it negotiated with its resources, it becomes possible to refrain from undesirable curtailments. Both the market-integrative and the service-centric roles of the VPP are verified through modeling and simulation with a benchmark European distribution network. The results confirm the added value of the proposed VPP in enhancing the integration of wind and solar power.

With the continuous promotion of power market reform, the trading mode of power market is constantly enriched. In this context, the virtual power plant technology breaks the traditional “source grid load storage” boundary, and realizes the multi-energy complementary and multi-source interaction of the power system, but the resource utilization strategy for the virtual power plant is not enough. Therefore, starting from the composition of virtual power plant, this paper constructs the internal resource quality evaluation system of virtual power plant. Based on the division of resource quality, the cascade utilization strategy of virtual power plant resources is provided. The practical results show that the quality evaluation of internal resources of virtual power plant is helpful to classify the internal resources of virtual power plant, help them realize the cascade utilization of resources, and maximize the economic benefits of virtual power plants.

The concept of virtual power plant (VPP) has been proposed to facilitate the integration of distributed renewable energy. VPP behaves similar to a single entity that aggregates a collection of distributed energy resources (DERs) such as distributed generators, storage devices, flexible loads, etc., so that the aggregated power outputs can be flexibly dispatched and traded in electricity markets. This paper presents an optimal scheduling model for VPP participating in day-ahead (DA) and real-time (RT) markets. In the DA market, VPP aims to maximize the expected profit and reduce the risk in relation to uncertainties. The risk is measured by a risk factor based on the mean-variance Markowitz theory. In the RT market, VPP aims to minimize the imbalance cost and wind power curtailment by adjusting the scheduling of DERs in its portfolio. In case studies, the benefits (e.g., surplus profit and reduced wind power curtailment) of aggregated VPP operation are assessed. Moreover, we have investigated how these benefits are affected by different risk-aversion levels and uncertainty levels. According to the simulation results, the aggregated VPP scheduling approach can effectively help the integration of wind power, mitigate the impact of uncertainties, and reduce the cost of risk-aversion.

China's “14th Five‐Year Plan” proposes that “carbon peak” is the basic prerequisite for achieving “carbon neutrality.” To achieve the “carbon neutrality” goal, the key is promoting the use of clean, low‐carbon energy and building a diversified clean energy supply system on the energy supply side. Wind energy, photovoltaics, and other clean energy sources are increasingly relying on electrification (P2G) technology to achieve utilization, and this phenomenon can help China achieve carbon neutrality goals as soon as possible and realize the carbon cycle. This article first aggregates combined heat and power (CHP), wind power, photovoltaics, P2G equipment, and gas storage devices into virtual power plants (VPPs), and the natural gas and electricity trading market are linked. We then take the virtual power plant operating profit maximization as the objective function considering the P2G device operating costs, carbon capture and storage costs. The optimization model is solved by particle swarm algorithm. Finally, the specific operating data of a gas‐thermal power virtual power plant system were selected to verify the feasibility of the proposed optimization model. We got some relevant conclusions. (1) The optimal scheduling of gas‐thermal power virtual power plant considering multiple energy markets can smooth the power load curve and improve the overall system benefit. (2) There is a negative correlation between the income of gas‐thermal power virtual power plant and the confidence level effectively. (3) The optimization model effectively increases the amount of carbon capture, which proves that the gas‐thermal power virtual power plant is effective for achieving China's carbon neutrality goal, it can be developed on a larger scale in future. The above results verify that the optimal operation mode of the gas‐thermal power virtual power plant is effective for achieving carbon neutrality.

Dynamic economic dispatch of a microgrid: Mathematical models and solution algorithm

Coordination of wind power producers with an energy storage system for the optimal participation in wholesale electricity markets

Harmony search meta-heuristic algorithm based on the optimal sizing of wind-battery hybrid micro-grid power system with different battery technologies

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Wireless Sensor Networks (WSNs) is a novel technology, which is developed with the advancement of micro-electronic, computing, and wireless communication technology. WSNs has merits of distributed information processing, covering broadly, and remote monitoring and control. Considering the disperse deployment, wind power resource random and seasonal characteristic in wind plant, we apply WSNs to SCADA system for wind power plant in this paper, which conquers the lack of disperse distribution, difficulty of remote monitoring in wind plant. Considering node cost, resource demand and communication reliability, the hardware of sensor node and sink node are based on 80C51 and ARM chips respectively, and μC/OS-II is selected as the ARM chip operating system. IDD-PC is adopted as routing scheme in wireless data transmission among nodes, which can balance node energy consumption, and be propitious to stability of the network structure. As to the energy limitation of WSN, the method of automatic compensating energy by wind generator is provided.

The Great Britain (GB) government is paving the path to decarbonisation by actively promoting the integration of wind power into its generation mix. This sharp transition to renewable energy, however, introduces specific challenges. The characteristics of non-synchronous wind turbines have the potential to impact grid stability and frequency security due to the reduction in system inertia. In response to these challenges, the deployment of virtual power plants (VPPs) is envisioned in deregulated power systems, which allow for the aggregation and coordinated control of diverse distributed energy resources, enhancing grid flexibility, reliability, and efficiency. Moreover, VPPs offer the provision of system inertia and frequency response services at the national level. This study drops this assumption and leverages the localised flexibility inherent in VPPs to meet the ancillary service requirements of the low-carbon power system. The formulated problem is constructed as a non-convex bi-level optimisation problem, where the upper-level problem represents the operation of a frequency-constrained unit commitment model, and the lower-level problem embodies the energy dispatches and frequency responses of a group of VPPs, guided by the dual price signals of cleared energy and ancillary services. To address the non-convex nature of the unit commitment problem, a two-fold approach is employed. First, binary commitment status decision variables are relaxed to continuous versions. Subsequently, the duality gap between the original non-convex unit commitment problem and its relaxed dual form is minimised, yielding a solution closely approximating the optimal solution of the original problem. Second, the relaxed bi-level optimisation problem is transformed into a single-level mathematical programs with equilibrium constraints by replacing the lower-level VPP problems with their equivalent Karush-Kuhn–Tucker optimality conditions. The case studies conducted in this work encompass a comprehensive scope. Initially, the study evaluates the effectiveness of VPPs in delivering ancillary services within the projected context of the GB power system for 2030. The findings reveal a substantial 28.28% decrease in system operation costs when compared to the benchmark case lacking VPP flexibility. Additionally, the integration of VPPs leads to significantly lower price signals for inertia, primary frequency response, and enhanced frequency response, reduced by 81.21%, 79.13%, and 86.27%, respectively, in contrast to the benchmark case. The analysis then delves into a sensitivity exploration, investigating the inherent flexibility of VPPs. The profit of local VPPs is expected to increase with VPP flexibility. Finally, the study illustrates how VPPs can adapt to and derive benefits from an increasing level of wind power penetration by providing a higher amount of frequency response.