Power Supply-demand Balance Research Articles

Innovative heat management method and metaheuristic algorithm optimized power supply-demand balance for PEMFC-ASHP-CHP system

The evolution of distributed building energy systems fuels the growing demand for sustainable energy solutions. In this paper, Proton Exchange Membrane Fuel Cell (PEMFC) and Air Source Heat Pump (ASHP) were integrated to form PEMFC-ASHP-CHP systems in three combination methods, i.e., Direct Combination (DC), Parallel Combination (PC), Series Combination (SC). And compared the energy management strategies and power balance of the system. To further improve reliability and flexibility, a diversion PEMFC-ASHP-CHP system was proposed by combining PC and SC's system advantages. Additionally, an iterative algorithm addressed the mismatch between power supply and demand. An empirical formula was proposed to improve iterative convergence speed for practical control situations. The coefficients were optimized using six metaheuristic algorithms, and the outcomes were summarized into an optimized operational plane to enhance the system control response speed further. The results show that the PC method performs better than DC and SC. It achieves a power consumption reduction of 52.8% and a COP improvement of 111.4% compared with the ASHP system. Meanwhile, the diversion system can more effectively utilize waste heat from the PEMFC and further improve the system's performance. The Queuing Search Algorithm (QSA) demonstrates superior accuracy for coefficient optimization. By using the established empirical formula, the convergence speeds of global and local iterative methods are improved by 26.10% and 41.78%, respectively, compared to the direct iterative algorithm. Ultimately, the optimized operational plane can achieve a maximum 37.16% hydrogen consumption reduction compared to the unoptimized system.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

A centralized EMPC scheme for PV-powered alkaline electrolyzer

Photovoltaic (PV)-powered alkaline electrolyzer system (PVPAES) is an advanced technique to convert the off-grid and intermittent PV-based solar energy into storable and transportable electrolyzer-based hydrogen energy with zero carbon emissions. However, it is difficult to realize the coordinated control of the off-grid PV module and the alkaline electrolyzer, due to the multiple timescale dynamics. To address this challenge, the PVPAES is decomposed into the slow part and fast part based on the dynamic time scale. Exploiting the decomposed subsystems, the slow one is assumed to be managed well by the auxiliary controller. For the fast one, a centralized economic model predictive control (CEMPC) scheme is constituted. This CEMPC integrates the energy management system and local feedback control into a single optimal control framework. A mathematical model of the PVPAES is established, on the basis of which the CEMPC directly adopts the economic indices as the cost function to realize the flexible power point tracking, power supply-demand balance, and dynamic economic optimization. Moreover, the inherent strong nonlinearity of PVPAES results in the nonconvex mixed-integer nonlinear programming optimization problem in the CEMPC. The exhaustive search algorithm utilizing finite converter switching states is adopted to achieve the global economic optimum. The effectiveness of the proposed CEMPC controller is illustrated through simulations under varying irradiance conditions.

A two-stage distributed optimization method for home energy management systems via multi-modal data-driven algorithm

The home energy management system (HEMS), which utilizes multi-modal data from multiple sensors to generate the knowledge about decision making, is essential to the optimization of home energy management efficiency. Load scheduling based on HEMS can improve the utilization efficiency of multi-modal data and derived knowledge, achieve power supply-demand balance, and reduce users’ electricity costs. This paper proposes a distributed load optimization scheduling method for the load scheduling problem in HEMS based on multi-modal data-driven algorithm. Additionally, a two-stage data-driven optimization method is proposed, including a first-stage optimization model based on minimizing electricity costs and a second-stage optimization model based on minimizing system load fluctuations. In the first stage, cost self-optimization is performed based on energy storage devices. In the second stage, a load optimization instruction is issued by the control center, and each user optimizes the load fluctuations based on the system load data. Compared to centralized control methods, this approach reduces the computational overhead of the control center. Finally, simulation experiments based on load scheduling in the HEMS are conducted. The results of the first optimization stage show that when the battery capacity integrated into the system increases from 3.68 kWh to 6.68 kWh, user costs can be reduced from 57.572 cents to 42.064 cents. It is not only evident that the proposed method can effectively save users on electricity costs, but the introduction of larger capacity batteries also lowers these costs. The second stage of load fluctuation optimization results show that the proposed method can effectively optimize the usage data of a group of users and decrease the absolute peak-valley difference by 8.8%.

The potential assessment of pump hydro energy storage to reduce renewable curtailment and CO2 emissions in Northwest China

Pumped hydro energy storage (PHES) can effectively alleviate the renewable curtailment and resource waste caused by expansion of wind and solar-based renewable energy (RE) sources. However, the influences of regional hydrological characteristics, operational characteristics of PHES units, and power supply-demand balance on the regulating effect of PHES have been neglected. In this paper, an improved hybrid power system dispatch model (Dispa-set) that considers regional hydrological characteristics, unit operational characteristics, and RE curtailment penalty is proposed. The potential and limitations of PHES on reducing renewable curtailment and carbon emissions in four Northwest provinces (FNP) during the 14th Five-Year Plan (FYP) are evaluated through 6 sub-scenarios. Studies have shown that increasing PHES can effectively reduce renewable curtailment and carbon emissions, but uncoordinated development of PHES and RE results in resource waste and slows down carbon reduction. For example, a 10% increase in PHES during the 14th FYP could increase renewable curtailment in Qinghai by 1.1% and CO2 emissions in Gansu by 0.05%, due to uncoordinated development, demand for electricity supply, and regulated power sources. Therefore, combining PHES development with regional hydrological and operational characteristics is essential to achieve coordinated development with RE and unlock technology, economic, and environmental benefits.

Consensus-Based Distributed Optimal Dispatch of Integrated Energy Microgrid

In recent years, the energy form of microgrids is constantly enriching, while the decentralization requirements of microgrids are constantly developing. Considering the economic benefits of an integrated energy microgrid (IEM), this paper focuses on the distributed optimal dispatch of IEM based on a consensus algorithm. The microgrid structure and multi-agent system are combined organically to get the decentralized architecture of IEM. This paper takes the incremental cost rate of each unit in IEM as a consensus variable. Based on the consensus theory, iterative optimization is carried out to achieve the optimal economic operation and power supply-demand balance of IEM. The distributed optimal dispatch is realized, and the convergence of the algorithm is proved. The experiment is carried out with LabVIEW and MATLAB and verifies the effectiveness of the algorithm. The results show that the distributed optimal dispatch algorithm can effectively reduce the power generation cost of the integrated energy system.

A novel information gap decision theory‐based demand response scheduling for a smart residential community considering deep uncertainties

AbstractDemand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.

A Multi-Timescale Allocation Algorithm of Energy and Power for Demand Response in Smart Grids: A Stackelberg Game Approach

The proliferation of renewable generation brings challenges to the power supply-demand balance. To relieve the power fluctuations caused by photovoltaic (PV) generation variability, a multi-timescale allocation algorithm that considers the allocation of available energy and power is proposed. Contracted demand response energy (CDRE) refers to the regulation energy, which is specified in contracts in advance, that can be used to relieve power fluctuations. First, the capacity of CDRE and available regulation power (RP) provided by load aggregators (LAs) that aggregate different demand response resources (DRRs) are estimated. In hour-timescale, CDRE is allocated to maximize the control economy of all participants, where a sample average approximation-based Stackelberg game is proposed to optimize the behavior of each participant based on considering PV generation uncertainty. In minute-timescale, RP is allocated to smooth the power fluctuations and minimize the power deviations based on the allocated CDRE results. Simulation on a modified IEEE-24 bus system verifies the effectiveness of the proposed algorithm in terms of reducing the power supply-demand imbalance with maximum revenue.

Push-Based Distributed Economic Dispatch in Smart Grids Over Time-Varying Unbalanced Directed Graphs

This paper is dedicated to solving the economic dispatch problem in smart grids, which aims at minimizing the total generation cost while satisfying the power supply-demand balance and generation capacity constraints. A distributed algorithm is proposed to solve the economic dispatch problem over directed communication topologies that are time-varying unbalanced. Its features are that the proposed algorithm is push-based, and the model of line losses is integrated into the problem of our interest to reduce the power losses. Resorting to geometric graph theory and convex analysis, it is proved that the incremental costs achieve consensus at a convergence rate O(1 / √t ) with t being the number of iterations. Furthermore, robustness of the proposed algorithm is investigated solving the EDP when the communication information undergoes arbitrary large but bounded constant delays and the local gradients involve observation noises with zero-mean and bounded variance. Finally, three case studies implemented on an islanded microgrid, the IEEE 30-bus system and the IEEE 118-bus system are tested to demonstrate the effectiveness of the proposed algorithm.

Trends of Simulation Technologies of Power Supply-Demand Balance and Frequency Control in Power Systems

The penetration of variable renewable energy sources such as photovoltaic and wind power generation is increasing rapidly, and the balancing market is going to be established in Japan in 2021. Because both of these events affect power supply-demand balance and frequency control, the simulation technologies of power supply-demand balance and frequency control are becoming more important to investigate the technical challenges and the solutions. Hence in this paper, the trends of these technologies are reviewed.

Development of Power Supply-Demand Balance and Frequency Simulation Models of All TSO's Control Area in Japan

Development of Power Supply-Demand Balance and Frequency Simulation Models of All TSO's Control Area in Japan

Impacts of hybrid time-varying tariffs on residential electricity demand: The case of Zhejiang Province

With the further liberalization of China's electricity market, electricity price becomes more important in the power supply-demand balance. We investigate the impacts of a hybrid time-varying tariff policy on residential electricity demand. In our estimation, we maximize the residential users' utility, taking average price responsiveness as their perceived price. Four indices related to power usage and expenses are designed to measure the response effects of time-varying tariff policy using a case study of Zhejiang province.

Risk-Limiting Real-Time Economic Dispatch in a Power System with Flexibility Resources

The integration of numerous intermittent renewable energy sources (IRESs) poses challenges to the power supply-demand balance due to the inherent intermittent and uncertain power outputs of IRESs, which requires higher operational flexibility of the power system. The deployment of flexible ramping products (FRPs) provides a new alternative to accommodate the high penetration of IRESs. Given this background, a bi-level risk-limiting real-time unit commitment/real-time economic dispatch model considering FRPs provided by different flexibility resources is proposed. In the proposed model, the objective is to maximize the social surplus while minimizing the operational risk, quantified using the concept of conditional value-at-risk (CVaR). Energy and ramping capabilities of conventional generating units during the start-up or shut-down processes are considered, while meeting the constraints including unit start-up/shut-down trajectories and ramping up/down rates in consecutive time periods. The Karush–Kuhn–Tucker (KKT) optimality conditions are then used to convert the bi-level programming problem into a single-level one, which can be directly solved after linearization. The modified IEEE 14-bus power system is employed to demonstrate the proposed method, and the role of FRPs in enhancing the system flexibility and improving the accommodation capability for IRESs is illustrated in some operation scenarios of the sample system. The impact of the confidence level in CVaR on the system operational flexibility is also investigated through case studies. Finally, a case study is conducted on a regional power system in Guangdong Province, China to demonstrate the potential of the proposed method for practical applications.

Trans-regional transmission of large-scale hydropower: problems and solutions in receiving power grid

Large-capacity hydropower transmission from southwestern China to load centers via ultra-high voltage direct current (UHVDC) or ultra-high voltage alternating current (UHVAC) transmission lines is an important measure of the accommodation of large-scale hydropower in China. The East China Grid (ECG) is the main hydropower receiver of the west-east power transmission channel in China. Moreover, it has been subject to a rapidly increasing rate of hydropower integration over the past decade. Currently, large-scale outer hydropower is one of the primary ECG power sources. However, the integration of rapidly increasing outer hydropower into the power grid is subject to a series of severe drawbacks. Therefore, this study considered the load demands and hydropower transmission characteristics for the analysis of several major problems and the determination of appropriate solutions. The power supply-demand balance problem, hydropower transmission schedule problem, and peak- shaving problem were considered in this study. Correspondingly, three solutions are suggested in this paper, which include coordination between the outer hydropower and local power sources, an inter-provincial power complementary operation, and the introduction of a market mechanism. The findings of this study can serve as a basis to ensure that the ECG effectively receives an increased amount of outer hydropower in the future.

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

Initialization-free privacy-guaranteed distributed algorithm for economic dispatch problem

This paper considers the economic dispatch problem for a network of power generators and customers. In particular, our aim is to minimize the total generation cost under the power supply–demand balance and the individual generation capacity constraints. This problem is solved in a distributed manner, i.e., a dual gradient-based continuous-time distributed algorithm is proposed in which only a single dual variable is communicated with the neighbors and no private information of the node is disclosed. The proposed algorithm is simple and no specific initialization is necessary, and this in turn allows on-line change of network structure, demand, generation constraints, and even the participating nodes. The algorithm also exhibits a special behavior when the problem becomes infeasible so that each node can detect over-demand or under-demand situation of the power network. Simulation results on IEEE 118 bus system confirm robustness against variations in power grids.

Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

In this paper, a class of mixed H 2 / H ∞ controller is designed for an energy router (ER) within the scenario of an energy Internet (EI). The considered ER is assumed to have access with photovoltaic panels, wind turbine generators, micro-turbines, fuel cells, diesel engine generators, battery energy storage devices, flywheel energy storage devices, loads, and other ERs. Two types of control targets are considered. First, due to the access of large-scale renewable energy sources, the DC bus voltage deviation within the ER system shall be regulated. Second, an optimal energy management strategy shall be achieved, such that the autonomous power supply-demand balance within each ER is achieved with priority and the rational utilization of controllable power generation devices and energy storage devices are realized. When these objectives are considered simultaneously, the control issues with respect to ER is formulated as a mixed robust H 2 / H ∞ control problem with analytical solutions provided. Several numerical examples are given, and the feasibility and effectiveness of the proposed method are demonstrated.

Fuzzy decision based energy dispatch in offshore industrial microgrid with desalination process and multi-type DGs

The marine economy has driven a quick development of offshore industrial infrastructure on the offshore islands in recent years. Due to the limitation of the geographical area and the shortage of power supply from the bulk electric power system, power supply from various forms of small-scale distributed generations (DGs) becomes a viable solution. However, these DGs are intermittent and stochastic in nature, which result in mismatch between generation and demand, increased fault level and degraded performance. This paper presents a fuzzy decision based approach and controller design method to address the energy dispatch in offshore industrial microgrid consisting of various forms of distributed generators (renewable sources and onsite diesel generators) and the seawater desalination system. The proposed fuzzy decision based dispatch solution is implemented fully considering the power generation and consumption relationship, the remaining amount of freshwater the state of charge of storage units, and without the requirement of accurate system models. The performance is validated through a case study and the numerical result demonstrates that the proposed fuzzy decision based solution can efficiently optimize the renewable energy utilization to meet the power supply-demand balance in offshore microgrid as well as maintain the fresh water supply stability.

Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network

A virtual power plant (VPP) is aimed to integrate distributed energy resources (DERs). To solve the VPP economic dispatch (VPED) problem, the power supply-demand balance, power transmission constraints, and power output constraints of each DER must be considered. Meanwhile, the impacts of communication time delays, channel noises, and the time-varying topology on the communication networks cannot be ignored. In this paper, a VPED model is established and a distributed primal-dual sub-gradient method (DPDSM) is employed to address the presented VPED model. Compared with the traditional centralized dispatch, the distributed dispatch has the advantages of lower communication costs and stronger system robustness, etc. Simulations are realized in the modified IEEE-34 and IEEE-123 bus test VPP systems and the results indicate that the VPED strategy via DPDSM has the superiority of better convergence, more economic profits, and stronger system stability.

Distributed Coordination Load Shedding of Islanded Microgrids Based on Sub-Gradient Algorithm

This paper proposes a novel distributed coordination load shedding (DCLS) approach for an islanded microgrid (MG) using sub-gradient algorithm of multi-agent system. The main objective is to achieve practical and optimal LS and obtain an optimum amount of load to be shed in a fully distributed manner under large disturbances. To coordinate the controllable loads (CLs) in an MG, an LS level (LSL) is first defined and evaluated locally to take the CL capacity and the LS willingness into consideration. Then, by updating the LSLs and the local frequency deviation measured based on frequency-inertia dynamics response, the proposed DCLS can be accomplished based on the sub-gradient algorithm. More importantly, only local information is needed to be updated during the entire DCLS process. Hence, the power supply demand balance can be well maintained, the utilization of LS can be significantly improved, and the requirements for communication topology changes can be adaptively met in a fully distributed way. The simulation results indicate that the proposed sub-gradient-based distributed coordination algorithm and corresponding DCLS are effective and adaptive.