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Coordinated Operation Strategy of Multi-energy Flow System with 5G Base Station Participating in Comprehensive Demand Response
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The operations of IoT devices (IoTD) and 5G base station (BS) contribute to most of carbon emission and on-power energy consumption in wireless edge network. To reduce operational costs and achieve low-carbon computing, this paper investigates a long-term average operational expenditure (OPEX) minimization problem, and proposes an online joint energy-network resource scheduling algorithm, including computation offloading, wireless power transmission, energy sharing, and task migration in wireless edge network powered by renewable energy, energy storage, and power grid. Differing from existing works, ours consider the energy loss of battery, dynamic carbon emission related with on-power energy, and constraint of spatial electric network into our model. Then, we apply the Lyapunov technique to decompose the proposed problem into three real-time sub-problems. Furthermore, a federal gradient descent based full-distributed online algorithm is proposed to obtain solution while protecting the privacy of network operators. We also prove the convergence of proposed algorithm and provide the trade-off between network stability and optimal OPEX. Simulation results reveal that the proposed algorithm outperforms existing benchmarks in reducing on-grid power dependence and OPEX.
Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries’ backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement.
Based on the high proportion of renewable energy connected to the active distribution network, this article studies the joint planning of demand-side response and energy storage. Firstly, a two-level optimization model is established for the planning of active distribution network. The upper level objective function is the investment, operation and maintenance cost of energy storage and fan, and the lower level objective function is the annual network loss cost. Then, the improved Longhorn algorithm is used to solve the two-layer programming model, and the combination of Longhorn beard algorithm and particle swarm optimization algorithm not only improves the iteration speed, but also improves the global searching ability. Finally, the example analysis proves the effectiveness of joint planning to solve the high proportion of renewable energy, thus improving the economic benefits.
The development of renewable energy represented by wind, photovoltaic and hydropower has increased the uncertainty of power systems. In order to ensure the flexible operation of power systems with a high proportion of renewable energy, it is necessary to establish a multi-scenario power system flexibility evaluation method. First, this study uses a modified k-means algorithm to cluster operating scenarios of renewable energy and load to obtain several typical scenarios. Then, flexibility evaluation indices are proposed from three perspectives, including supply and demand balance of the zone, power flow distribution of the zone and transmission capacity between zones. Next, to calculate the flexibility evaluation indices of each scenario—and according to the occurrence probability of each scenario—we multiplied the indices of each scenario by the scenario occurrence probability to obtain comprehensive evaluation indices of all scenarios. Based on the actual historical output data of renewable energy and load of a southern power system in China, a flexibility evaluation was performed on the modified IEEE 14 system and modified IEEE 39 system. The results show that the proposed clustering method and flexibility indices can effectively reflect the flexibility status of the power system.
Operation optimization is a critical issue in the management of the multi-source large-scale urban centralized heating system (UCHS). A major challenge is to deal with the complex topological structure of its heating network. We investigate the operation enhancement of UCHS from the view of network splitting. We carry out the network splitting analysis of UCHS by establishing its heating network model and corresponding simulation platform based on graph theory. We validate the model against experimental data in a city located in northern China, and further use the model to show the effectiveness of network splitting by calculating its energy consumption indices under different splitting schemes. Simulation results show that the optimal network splitting scheme could improve the load distribution among the heating sources and reduce the operation cost. We apply the scheme during the 2018–2019 heating season and compare the results with the previous season operated by its existing scheme. The optimized scheme reduces heat supply requirements for heating sources and improves the load distribution among heating sources. With the weather condition change considered, seasonal accumulated area specific daily heat supply is reduced by 3.9%. Overall, seasonal natural gas consumption is reduced by 16.8%.
Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.
To make full use of abundant new energy sources and meet the diverse energy demands of users, a system with new energy resources could be used to realize a combined cooling, heating, and power (CCHP) system. Environmental pressure can be reduced when new energy is used as a heat source or auxiliary power source. However, many kinds of energy can lead to uncertainty, low energy utilization rate, and high cost with the increasing types of energy. Therefore, an evaluation method of the advantages and disadvantages of a CCHP system coupled with wind and solar (Wind-Solar-CCHP) is needed. Firstly, emergy theory is introduced to evaluate the sustainable development level of the two systems, and a new comprehensive evaluation index system is established by combining emergy indexes with traditional indicators. Secondly, a multi-objective decision-making (MODM) method is proposed based on fuzzy-analytic hierarchy process (Fuzzy-AHP), anti-entropy weighting (AEW), and game theory to calculate the weights of these indexes. Then, the Kendall rank correlation coefficient is used to determine the correlation between the CCHP and Wind-Solar-CCHP systems. Taking the CCHP system as a reference, the integrated performance of the systems is analyzed by using the fuzzy comprehensive evaluation (FCE) method. Finally, an example of a hotel in a city in western China is selected for verification. The accuracy and robustness of the proposed method were verified by sensitivity analysis. The obtained experimental results indicate that the comprehensive performance of the Wind-Solar-CCHP system was superior to that of the CCHP system. Compared with other methods in the literature, the proposed method could achieve unification of subjective and objective attribute weights, overcome the limitations of the existing single evaluation method, and make the evaluation results more accurate. Moreover, these research results can provide a reference for the comprehensive utilization of new energy in China.
Regional energy internet is an important application of “Internet+” smart energy in China’s new urbanization process. The evaluation of regional energy internet is a significant way to measure the development level of regional energy internet. However, such work is a blank of current research. Therefore, the purpose of this study is to evaluate regional energy network from a comprehensive view. Firstly, this study establishes an evaluation criteria system for regional energy internet, which consists of four dimensions (technical, economic, social, and engineering) and associated 16 criteria. Secondly, considering the uncertainty involved the evaluation process, cloud model is utilized to depict the fuzziness and randomness, which are indispensable elements in the uncertainty. Thirdly, a fuzzy analytic hierarchy process, which is a combination of AHP and fuzzy set theory, is developed to determine the weights of the identified dimensions and criteria. Finally, the evaluation results in practical case in China show that the comprehensive evaluation grade of the demonstration project is good. And the technical dimension and economic dimension should be noted, mainly involving comprehensive efficiency and energy economy level. The results of the evaluation are exactly consistent with the actual situation, indicating that the evaluation method has strong practical value.
Forecasting energy consumption in buildings is crucial for achieving effective energy management as well as reducing environmental impacts. With the availability of large amounts of relevant data through smart metering, gas consumption forecasting is becoming an integral part of smart building design so that these requirements are met. In this study, we investigate week-ahead forecasting of daily gas consumption in three types of buildings characterized by different gas consumption profiles during a five-year period. As gas consumption in buildings is highly correlated with the average outdoor temperature, regression models with additional residual modeling are used for forecasting. However, conventional regression models with autoregressive moving averages (ARMA) errors (regARMA) perform poorly when the temperature forecasts are inaccurate. To address this, a new forecasting model termed genetic-algorithm-optimized regression wavelet neural network (GA-optimized regWANN) is proposed. It uses the wavelet decomposition of the residuals of temperature regression time-series, which are modeled by multiple nonlinear autoregressive (NAR) models based on sigmoid neural networks. The appropriate delays in the regression vectors of the NAR models are selected using a binary GA. Compared with regARMA and seasonal regARMA, the GA-optimized regWANN model achieved in the three buildings a reduction of 22.6%, 17.7%, and 57% in the mean absolute error (MAE) values in ex post forecasting with recorded temperatures, and a 52.5%, 27%, and 43.6% reduction in the MAE values in ex ante forecasting with week-ahead forecasted temperatures, even under conditions of relatively significant errors in the forecasted temperature.
With the development of active distribution network (ADN) and the increase of penetration rate of new energy such as wind power and photovoltaic, the optimal operation problem of ADN is facing challenges. So, this paper proposes a multi-time scale optimal dispatch method based on mixed-integer linear programming to improve the calculation speed of the traditional dispatching model. First, the linear branch power flow of distribution network is introduced, and the main elements in ADN are modeled, meanwhile, the linearization method is used to simplify the non-linear components. Then, the day-ahead dispatch model is established by considering the active and reactive power coordination. In addition, in order to reduce the influence of the stochastic power from DG and FL on dispatch results, taking the results of day-ahead dispatch as reference, an auxiliary short-term dispatch model is also constructed to maximize the consumption of wind and photovoltaic (PV) power. Finally, the validity and reliability of the proposed method are verified by a modified IEEE 33-node distribution system.
Utilizing the flexibility provided by the thermal system components, for example, pipelines in the district heating network (DHN), building envelopes as well as thermal energy storage (TES) devices, can be an effective way for power system to solve the wind curtailment problem which closely relates to the limited flexibility of combined heat and power (CHP). However, almost all the previous studies ignored heat transfer (HT) constraints when modelling these abovementioned thermal system components. HT constraint is one of the most important constraints in thermal system analysis, without considering it may result in infeasibility for the dispatch results. Hence in this study we propose a detailed model for these three components, namely DHN, building envelopes as well as TES devices, with consideration of HT constraints. This model can be further simplified to consider different combinations of these components by setting relevant model parameters. An iteration solution to deal with such nonlinear HT constraints is put forward. The effectiveness of the proposed model is verified by case studies. The simulation results demonstrate that considering HT constraints is necessary when try to exploit the flexibility provided by these thermal system components, or we may overestimate their benefit