Real-time Power Balance Research Articles

Multi-Objective Reliable Optimization Dispatch of High-proportion Power System Based on Improved Particle Swarm Algorithm

Abstract Energy storage technology is pivotal for managing high-proportion power system consumption and maintaining real-time power balance. The optimal configuration of energy storage is influenced by factors such as capacity, wind power loads, and output power. This paper utilizes dynamic programming to evaluate the operational efficiency of conventional units and wind turbines within the power system, followed by the application of an enhanced particle swarm optimization algorithm to tackle optimization challenges. A BP neural network is employed to predict the impact of different units on the power balance of the micro-grid, aiming to achieve maximum comprehensive benefits and minimize investment costs. A reliability model is proposed to balance power storage and load release, thereby improving system stability and reliability. Simulation results demonstrate that dynamic programming combined with enhanced particle swarm optimization can reduce the operating costs of the micro-grid, highlighting the practical advantages of power system energy storage technology.

Simultaneous costs minimizing in electricity and gas micro-grids with the presence of distributed generation

Distributed generation can actively participate in the day-ahead markets, real-time power balance, and wholesale gas markets to achieve various goals, such as supplying gas to various electric power generation plants. A multi-objective network with two types of loads is considered in this paper. The reason for the simultaneous optimization of these two networks is that these two energy carriers are dependent on each other and gas is needed to produce electricity, so this issue can be addressed with a multi-objective function. The simulation carried out in this article is coded in GAMS software as a mixed integer linear programming (MILP). The efficiency of gas turbines and fuel cells in this article is dependent on their working point, and considering the exact model of these resources and the relationships related to the calculation of their fuel consumption is non-linear. On the other hand, a binary variable has been used to show the charging and discharging state of the storage and the on-and-off state of the gas turbines. Therefore, the problem considered in this article is a MILP problem. The results of this article are the proper planning of charging and discharging of the energy storage system with the proper planning of the power generation of different energy sources considering the network loads in two optimized and non-optimized scenarios.

MMD-DRO based economic dispatching considering flexible reserve provision from concentrated solar power plant

To achieve real-time power balance between uncertain resource and demand consumptions in locally standalone power systems with a high penetration of new energy, it is promising to build concentrated solar power (CSP) plants through the establishment of power generation and flexible reserve provision. To address that, a refined maximum mean discrepancy-distributionally robust optimization (MMD-DRO) based economic dispatching model considering flexible reserve provision from CSP plants is designed in this work. Remarkably, a maximum mean discrepancy (MMD)-based ambiguity set representing new energy uncertain outputs is carried out. This proposed MMD-based ambiguity set mathematically keeps the shape of an ellipsoid through kernel mean embedding and related reformulation, and its center is equivalated as a group of empirical distributions extracted from historical data. On this basis, the impact of CSP and relevant flexible reserve capabilities in combination with conventional thermal power units is analyzed, and the joint up-forward/down-forward reserve provision produced from thermal power units and CSP is derived. Furthermore, a two-stage economic dispatching model considering flexible reserve configurations is constructed, which can be solved by the column & constraint generation algorithm. Some results on a modified IEEE 30-bus test system have demonstrated the effectiveness of the proposed model.

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

Optimal economic dispatching strategy for power systems considering distributed controllable load clusters

The day-ahead dispatching of power system depends on the predicted renewable energy output and load power, which brings great challenges to the real-time power balance. This paper focuses on voltage sensitive loads (VSLs) that are widely distributed in space, thus exploring the real-time dispatching potential for VSLs. Firstly, based on the P-V relationship of VSLs clusters, a ZIP model is adopted to establish VSLs’ power controllable model. Meanwhile, considering the potential voltage instability risk during the process of voltage regulation, a linear relationship between voltage safety margin (VSM) index and node power variation was derived. Then, a two-stage linear dispatching model for controllable VSLs clusters was established to meet the needs of online application. In detail, the first stage mainly focuses on evaluating the scheduling potential of VSLs clusters and the second stage determines the optimal real-time response strategy for VSLs clusters. Finally, the effectiveness and feasibility of the proposed method are verified through an improved IEEE-30 node system.

Bi-objective Pareto optimization for clustering-based hierarchical power control in a large-scale PV power plant

To assist the power grid for the real-time power balance, more and more PV power plants have participated in automatic generation control (AGC). In this process, a PV power plant should distribute the dynamic total AGC signal to all the controllable inverters. Firstly, it is difficult to satisfy multiple operation requirements for the whole PV power plant. Secondly, it easily results in a high optimization complexity due to the large amount of controllable inverters, especially for a large-scale PV power plant. To handle these two problems, a clustering-based hierarchical power control is constructed with two conflict objectives, including the AGC responding performance and the reactive power reserve. With the discrepant responding performance of different inverters, all the inverters are clustered into multiple groups by the K-medoids algorithm according to their regulation parameters. Hence, the hierarchical power control contains a top-layer control and a bottom-layer control. An efficient non-dominated sorting genetic algorithm is adopted to acquire the Pareto optimal solutions for the top-layer control, then the bottom-layer control is implemented in proportion to their regulation capacities. Finally, the case studies are carried out to verify the proposed technique for a large-scale PV power plant under different AGC signals and dynamic irradiations.

Reactive Power Compensation Configuration of Offshore Wind Power Based on Economic Differential Pressure Theory

The conventional reactive power balance analysis method for offshore wind power transmission projects can be used to obtain many feasible solutions for reactive power compensation configuration, which requires a large number of simulation calculations to eliminate unreasonable schemes. The work is tedious and lacks theoretical guidance. In this paper, the main influencing factors, such as the current carrying capacity of offshore wind power and submarine cable, the mode of real-time power generation, and the reactive power output regulation of the wind turbine itself, are considered. The reactive power balance of offshore wind power transmission projects is derived. Combined with the economic differential pressure theory, the sea-side and land-side configuration methods of high reactance and dynamic reactive power compensation are put forward. On the basis of realizing real-time reactive power balance, the recommended reactive power compensation configuration scheme effectively reduces the active power loss of the transmission project, and at the same time, lowers the bus voltage of each node at sea, thus reducing the high risk of the excessive voltage of offshore wind farm. This project is supported by the Science and Technology Project of State Grid Corporation of China under “Research on multiple temporal and spatial coordinated optimal control theory for medium and long-distance large-scale offshore wind power transmission system” (No. 5100-202155305A-0-0-00).

Frequency Regulation of an Islanded Microgrid Using Hydrogen Energy Storage Systems: A Data-Driven Control Approach

Hydrogen energy storage (HES) systems have recently received attention due to their potential to support real-time power balancing in a power grid. This paper proposes a data-driven model predictive control (MPC) strategy for HES systems in coordination with distributed generators (DGs) in an islanded microgrid (MG). In the proposed strategy, a data-driven model of the HES system is developed to reflect interactive operations of an electrolyzer, hydrogen tank, and fuel cell, and hence the optimal power sharing with DGs is achieved to support real-time grid frequency regulation (FR). The MG-level controller cooperates with a device-level controller of the HES system that overrides the FR support based on the level of hydrogen. Small-signal analysis is used to evaluate the contribution of FR support. Simulation case studies are also carried out to verify the accuracy of the data-driven model and the proposed strategy is effective for improving the real-time MG frequency regulation compared with the conventional PI-based strategy.

A Contract Theory Approach to Dynamic Incentive Mechanism and Control Synthesis for Moral Hazard in Power Grids

This article proposes a synthesis of a novel dynamic incentive mechanism and optimal control for moral hazard in power grids with real-time power balancing markets. For the liberalization of the balancing markets, it is an indispensable requirement to integrate dynamic subsystems controlled by individual agents into a stabilized power grid via an incentive mechanism. Meanwhile, without the proper use of the agents’ private information, high-speed market-clearing achieving an appropriate social objective cannot be ensured. Consequently, a market mechanism is needed that contains some dynamic incentive mechanisms for eliciting their private information and proper controls under moral hazard, which is called incentivizing market in this article. Drawing on dynamic contract theory in microeconomics, a novel method for designing the incentivizing market is proposed on the basis of the integration of economic models and a dynamic grid model. Conventional contract problems have been analyzed for both static and dynamic systems whose control inputs are operated directly by an independent system operator. However, these analyses do not accord with the design for the dynamic incentive mechanism in the incentivizing market. The approach in this article requires adapting the dynamic contract problems to the market. To achieve this objective, the fundamental formulas for optimal design are established and the basic properties of the designed market based on a model-based approach are clarified. We next propose optimal pricing and control synthesis for social-objective designs. The performance of our proposed method is illustrated through simulations with an Institute of Electrical Engineers of Japan (IEEJ) East 30-machine system.

Blockchain-Based Trustworthy Energy Dispatching Approach for High Renewable Energy Penetrated Power Systems

Renewable energy sources (RES) and low-carbon technology users play a vital role in modern power systems. However, RES generation is easily affected by the environment. Meanwhile, the load, such as electric vehicles (EVs) and prosumers, accounts for most low-carbon technology users. Their power is usually superimposed on peak loads without dispatching, which also exacerbates the instability of the power system. Current optimal dispatching mechanisms mainly rely on centralized organizations, while their dispatching process is not open and transparent. In this article, we propose a blockchain-based trustworthy dispatching approach for the distribution network in high renewable energy penetrated power systems. We first develop an optimal dispatching model considering EVs’ charging behavior and the prosumers’ economic benefits. With the model, prosumers can be dispatched to balance power and consume renewable energy, reducing the impact of disorderly charging on the grid and the abandonment of RES generation. An orderly charging iteration optimization (OCIO) algorithm is proposed to implement orderly EV charging while considering the charging cost and the period. We also propose a modified particle swarm optimization (mPSO) algorithm to publish dispatching tasks based on real-time power balance. Furthermore, blockchain is applied as an open and transparent ledger to record each entity’s power generation and consumption information, ensuring that the dispatching process is trustworthy. Finally, the effectiveness of the dispatching approach is verified in the modified IEEE 33-bus test system and Ethereum-based smart contracts.

Scenario-based robust energy management of CCHP-based virtual energy hub for participating in multiple energy and reserve markets

The multi-energy systems can operate and schedule the distributed energy resources (DERs) locally to supply the multi-type loads and participate in the energy markets by aggregating the output power of DERs. Recently, the virtual energy hub (VEH) concept, derived from the energy hub and virtual power plant concepts, has been proposed for participating in the electrical and thermal markets. In this paper, robust self-scheduling of a VEH for participating in the energy and reserve markets is presented. The thermal reserve market is proposed to maintain the real-time thermal power balance and compensate for the effects of thermal demand uncertainty. Various types of DERs for supplying loads of each energy carrier are considered. Compressed natural gas (CNG) station is discussed and modeled linearly in the developed VEH to provide the fuel needed by Hybrid CNG and plug-in electric vehicles, which used the CNG as their secondary energy resource. A scenario-based robust approach is developed and presented to maximize the VEH profit and control the downside risk without adding surplus constraints. Finally, the proposed model is simulated in three case studies to evaluate its performance and effectiveness. • Self-scheduling of virtual energy hub for participating in the bilateral markets. • Thermal and electrical reserve markets. • Hybrid CNG and Plug-in electric vehicles. • CNG station for supplying needed CNG fuel. • Downside risk control via developed scenario-based robust method.

Towards energy-efficient and cost-effective DC nanaogrid: A novel pseudo hierarchical architecture incorporating V2G technology for both autonomous coordination and regulated power dispatching

Most recently, DC nanogrid incorporating effective energy management has attracted widespread attention. Due to its favorability to integrate renewable energy sources and emerging power electronic loads, such as photovoltaics and electric vehicles (EVs), DC nanogrid is believed to be able to improve the energy utilization efficiency and mitigate the carbon footprint in the coming decades. Towards a compact, cost-effective, and easy-to-build energy management scheme for nanogrid, this paper presents a pseudo hierarchical management architecture built upon the smart charging point. The proposed architecture incorporates the upper-level central controller with the local power role creatively and comprises two timescale management levels with corresponding operation strategies. In the short-timescale local management level, a state-triggered droop strategy based on the decentralized control mechanism is introduced to realize the autonomous power coordination without extensive communication links. The autonomous vehicle-to-grid (V2G) operation is also implemented with providing real-time power balance capability to unpredicted and short-timescale load variation in peak periods. In the power dispatching level, a multi-mode power dispatching strategy involving six operation modes is introduced to realize the efficient power scheduling for the nanogrid. The effectiveness of the proposed architecture and operation strategy is verified in the detailed simulation model and hardware-in-loop experiment platform. The results show that the real-time, autonomous, and stabilized power coordination in nanogrid could be realized along with a self-regulated V2G operation. Additionally, the peak-shaving and valley-filling of load curve, the satisfaction of EV charging demand, and an improved operation economy are achieved under the proposed architecture and operation strategy.

Fuzzy hierarchical analysis applied in high ratio wind power system energy storage

With the continuous development of wind power generation technology and the increasing investment, the global installed scale of wind power generation is growing rapidly. Since the strong intermittency and fluctuation of wind power easily leads to the difficulty of real-time power balance in system operation and the relatively expensive cost of energy storage, this paper will take the high proportion of wind power system as an example to analyze the configuration and operation of energy storage. Firstly, the generation plan curve of the unit is drawn and the system unit power supply cost is calculated, and finally the reliability of the model and the quadratic planning algorithm is checked by genetic algorithm. Secondly, the solution with the lowest overall system cost is solved. Finally, the model is built and combined with fuzzy hierarchical analysis to obtain the optimal power balance solution, and the feasibility and effectiveness of the solution are determined by consistency test.

Coordinated control for PV-ESS islanded microgrid without communication

Energy storage systems (ESSs) could help maintain real-time power balance of islanded microgrids, but they cost too much. In this paper we propose a coordinated control strategy without communication for an islanded microgrid, where less ESSs are installed. The microgrid is made up of photovoltaics (PVs) and ESSs inverters and loads. In the proposed method, PVs can achieve both MPPT operation and frequency regulation by using adaptive frequency droop. In the meanwhile, PVs can curtail the output power and switch to proportional droop to support the microgrid when ESSs are insufficient to absorb the surplus power. For ESSs, the droop control is adopted for grid-supporting ability until the limit of charging power is activated by the power limit controller. Generally, the control strategy for each unit is able to softly and autonomously switch to the proper mode based on the state of charge (SOC) of ESSs, the total load demand and the frequency level in the microgrid. Besides, the control strategy can coordinate the AC microgrid with arbitrary PVs and ESSs. Furthermore, the small-signal models of the microgrid in different modes are established to analyze the design of control coefficients. Finally, the proposed strategy is validated in Matlab/Simulink simulation and hardware-in-loop (HIL) tests under different cases.

Bi-level decentralized control of electric heating loads considering wind power accommodation in real-time electricity market

Flexible resources on demand-side, such as electric heating loads (EHLs), are perceived as an effective way of supporting the power balance of power grids with large scale renewable energy integration. However, it is difficult to quickly, effectively and evenly aggregate and schedule a large population of these loads because of their heterogeneity and geographic dispersion. The efficiency and effectiveness of optimal control are important for achieving the power balance between power source and load, especially in real-time scheduling. Moreover, the even load allocation between these heterogeneous loads also needs to motivate users participation in real-time power balance. To follow wind power in the real-time electricity market, this study presents a bi-level control method for EHLs based on distributed optimization computing, which considers the bi-level queuing method, different load control modes and multi-time scales between different control levels. First, each load group, which includes a large population of EHLs is controlled by a distributed server (DS); all the load groups are scheduled by the central management server (CMS), which deals with the wind farm or electricity retailer. Next, a distributed optimization model is built based on the bi-level control structure, in which the different power balance statuses and multi-time scales between the electricity market and EHLs are considered. Furthermore, considering the power balance on the multi-time scales, layer-by-layer load allocation optimization through the bi-level time-varying temperature queuing method, and the load-leveling optimization, serve as the sequential optimization problems. Finally, a simulation including 12,500 EHLs based on measured data in northern China is carried out. The results show that the proposed approach can effectively realize real-time power balance between wind power and EHLs, while maintaining uniform load allocation between the EHLs.

Cyber-enabled grids: Shaping future energy systems

As the penetration of distributed energy resources based on renewable sources increases, several technical challenges are introduced into energy grids. These include real-time power balance, control of bi-directional energy flow, power quality, distributed optimization, state estimation and topology estimation in distribution grids, and multi-level electricity trading. To overcome these challenges and to create situational awareness in energy grids, pervasive sensing becomes essential. To overcome hurdles such as implementation and cost of such pervasive sensing, in addition to traditional contact-sensing methods, contactless sensors that can measure key variables in the grid needs to be leveraged. Contactless sensing enables measurement of process variables that may be hard to measure due to technological limitations of contact sensing, large measurement delays, or high costs. In addition to contactless sensing, pervasive sensing proves advantageous as it can leverage ongoing technological advances in Internet of Things (IoT), as they can lead to enhanced network connectivity between sensors as well as between the edge and the cloud. Finally, pervasive sensing proves even more attractive by integrating contactless sensing not only with wireless communication but also with shielding and energy harvesting. This paper reviews pervasive sensing techniques in power grids that encompass contactless sensing technologies, IoT connectivity, energy harvesting and shielding. In addition, we also explore how pervasive sensing in a Cyber-Enabled Grid (CEG) can contribute to the development roadmap of Autonomous Energy Grids (AEGs), a futuristic concept where the grid will be making automated operational decisions. The potential challenges and research opportunities in this pioneering research field such as data deluge, cybersecurity, and sensor fusion will be discussed. This review article, which addresses the role of pervasive sensing in CEGs, is a first of its kind. It will help engineers and scientists to understand its significant potential to shape future energy systems.

A two-stage multi-objective optimal scheduling in the integrated energy system with We-Energy modeling

This paper proposes a two-stage multi-objective optimal scheduling strategy (TMOS) based on the innovative mathematical model of We-Energy (WE) in the integrated energy system (IES). WE, as a new-style energy unit with full duplex and multi-energy carrier coupling interaction, is necessary to provide a mathematical model to solve the schema translation problem for the scheduling of the WE. Therefore, a WE mathematical model based on Hadamard Product is presented which can clearly show the dynamic properties of internal elements and the full duplex characteristic of the WE. Namely, the optimization model for the WE can be easily and compactly established by utilizing the proposed method. Furthermore, in order to reduce the unfavorable effects of the renewable energy (RE) uncertainty and realize the energy management of the WE, a TMOS on account of the proposed mathematical model is presented to dispatch the WE operation. The comprehensive impact of multiple significant operation indicators is considered in TMOS which conventional methods ignored. The economic benefit and customer satisfaction can be improved by the first-stage of TMOS according to the energy price and the day-ahead forecasting of RE generation. Meanwhile, the TMOS can reduce the impact of the RE prediction error to realize the real-time power balancing and ensure the security operation by regulating the components of the WE in the second-stage dispatch. The proposed strategy is demonstrated by two example cases, where the performance of the TMOS is observed. The consequences of the cases are analyzed in view of the energy exchanges with networks and the outputs of elements in the presented condition. Moreover, the contrast of the proposed optimal scheduling with another traditional optimal method is also discussed in the paper. As the results shown in the cases, the TMOS based on the innovative WE model balances the forecast error and has more benefits in networks influence, customer satisfaction and residual capacity indicator.

Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture

With the integration of highly permeable renewable energy to the grid at different levels (transmission, distribution and grid-connected), the volatility on both sides (source side and load side) leading to bidirectional power flow in the power grid complicates the control mechanism. In order to ensure the real-time power balance, energy exchange, higher energy utilization efficiency and stability maintenance in the electric power system, this paper proposes an integrated application of blockchain technology on energy routers at transmission and distribution networks with increased renewable energy penetration. This paper focuses on the safe and stable operation of a highly penetrated renewable energy grid-connected power system and its operation. It also demonstrates a blockchain-based negotiation model with weakly centralized scenarios for “source-network-load” collaborative scheduling operations; secondly, the QoS (quality of service) index of energy flow control and energy router node doubly-fed stability control model were designed. Further, it also introduces the MOPSO (multi-objective particle swarm optimization) algorithm for power output optimization of multienergy power generation; Thirdly, based on the blockchain underlying architecture and load prediction value constraints, this paper puts forward the optimization mechanism and control flow of autonomous energy coordination of b2u (bottom-up) between router nodes of transmission and distribution network based on blockchain.

Asynchronous Economic Dispatch for Combined Heat and Power Systems

In the day-ahead economic dispatch of combined heat and power systems, the electric power system is adjusted in minutes because of the short dynamic time and the requirement of real-time power balance, while the heating system is dispatched in hours due to its large inertia and long dynamic process. However, the different dispatch time scales of electricity and heat are ignored by existing synchronous dispatch methods, which limits the improvement of system efficiency. To address this challenge, in this article, the asynchronous dispatch method is proposed and instantiated by two different dispatch models featuring a short electric dispatch time scale and a long heat dispatch time scale. Case studies demonstrate the asynchronous dispatch method can overcome the efficiency and reliability problems caused by the existing synchronous dispatch methods.

Intra-Hour Microgrid Economic Dispatch Based on Model Predictive Control

Renewable energy-based generation facilities emerging in microgrids are modifying many traditional principles of economic dispatch because of the variability and uncertainty of their output characteristics. Since the power generation from renewable resources is difficult to anticipate, a real-time adjustment of generation schedules is necessary after more forecast information becomes available. Intra-hour, or ultra-short-term dispatch, allows microgrid operators to frequently update generators’ outputs for providing power supplies efficiently in an uncertain operating condition. However, several technical challenges such as information availability, control architecture, and computational burden make it difficult to find an optimal generation schedule at runtime. This work addresses the intra-hour economic dispatch problem by designing a closed-loop distributed model predictive control (DMPC) that reduces potential variations in the determined generation schedules. At first, optimization problems regarding the behaviors of several participants in a microgrid are formulated. Then, the procedures for achieving the real-time power balance under ramp-rate constraints are detailed. Simulation results present the efficiency of the proposed DMPC for the optimal intra-hour economic dispatch when multiple types of generation resources are considered in a microgrid.