Optimal Dispatch of Multiple Photovoltaic Integrated 5G Base Stations for Active Distribution Network Demand Response

Abstract

Multiple 5G base stations (BSs) equipped with distributed photovoltaic (PV) generation devices and energy storage (ES) units participate in active distribution network (ADN) demand response (DR), which is expected to be the best way to reduce the energy cost of 5G BSs and provide flexibility resources for the ADN. However, the standalone PV-integrated 5G BS has the characteristics of wide distribution, small volume, and large load fluctuations, which will bring strong uncertainty to the ADN by directly participating in the DR. Therefore, a system architecture for multiple PV-integrated 5G BSs to participate in the DR is proposed, where an energy aggregator is introduced to effectively aggregate the PV energy and ES resources of 5G BSs. Then, a two-stage optimal dispatch method is proposed. Specifically, in the large-timescale DR planning stage, an incentive mechanism for multiple PV-integrated 5G BSs to participate in the DR is constructed based on the contract theory, which ensures that multiple 5G BSs respond to and satisfy the peak-shaving demand of the ADN. In the small-timescale online energy optimization stage, based on the energy sharing mode among 5G BSs, a Lyapunov-based online energy optimization algorithm is proposed to optimize the shared energy flow between the internal layer and the interactive layer of 5G BSs, which further improves PV absorption and ensures operation stability of ES in the 5G BS. Simulation results show that the proposed two-stage optimal dispatch method can effectively encourage multiple 5G BSs to participate in DR and achieve the win–win effect of assisting the ADN peak-shaving and low-carbon economic operation of 5G BSs.

Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud

“…Our purpose here is to shed light on the mechanics of the method within a context that complements the principal context of this work. Nine research units (RUs) [ 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 ] have been mined. The motivation driving this research is presented first, followed by a tabulation of the P-, A- and D-node codes and a categorization of the codes.…”

“…Motivation A clear sense of motivation emerges: OPEX (energy cost) must be controlled and greenhouse gas (GHG) emissions must be reduced. The Jevons paradox (alternatively expressed in the Khazzoom–Brookes postulate) emerges again: the deployment density of 5G base stations (BSs) and 64/32-channel massive multiple-input multiple-output (MIMO) antenna arrays drives “the power consumption of a 5G BS to be 2-3 times that of an ordinary 4G BS” [ 144 ]. Other works are motivated by the need to control capital expenditure (CAPEX) [ 142 , 147 , 148 ] and understand how RES harvesters compare with conventional hydrocarbon-fueled generators, therein seeking either to reduce CAPEX or to determine the true cost of wholly autonomous operation under the condition of power supply from RES.…”

“… 3 [ 143 ] How can the limitations of individual RES be overcome? 4 [ 144 ] Spatio-temporal distribution of light intensity and communication demand misaligns energy supply and load. 5 [ 144 ] Disparate ownership of RES plant and automatic distribution network (ADN) infrastructure leads to concerns with security and privacy.…”

“… 4 [ 144 ] Spatio-temporal distribution of light intensity and communication demand misaligns energy supply and load. 5 [ 144 ] Disparate ownership of RES plant and automatic distribution network (ADN) infrastructure leads to concerns with security and privacy. 6 [ 144 ] How can participation of communications service providers (CSPs) in renewable energy (RE) harvesting be encouraged?…”

“… 5 [ 144 ] Disparate ownership of RES plant and automatic distribution network (ADN) infrastructure leads to concerns with security and privacy. 6 [ 144 ] How can participation of communications service providers (CSPs) in renewable energy (RE) harvesting be encouraged? 7 [ 144 ] How can real-time energy scheduling be improved to avoid loss of RE, over-discharge of energy storage (ES) and balance between supply and demand?…”