Optimal Dispatch for Battery Energy Storage Station in Distribution Network Considering Voltage Distribution Improvement and Peak Load Shifting

Distribution networks are commonly used to demonstrate low-voltage problems.A new method to improve voltage quality is using battery energy storage stations(BESSs),which has a four-quadrant regulating capacity.In this paper,an optimal dispatching model of a distributed BESS considering peak load shifting is proposed to improve the voltage distribution in a distribution network.The objective function is to minimize the power exchange cost between the distribution network and the transmission network and the penalty cost of the voltage deviation.In the process,various constraints are considered,including the node power balance,single/two-way power flow,peak load shifting,line capacity,voltage deviation,photovoltaic station operation,main transformer capacity,and power factor of the distribution network.The big M method is used to linearize the nonlinear variables in the objective function and constraints,and the model is transformed into a mixed-integer linear programming problem,which significantly improves the model accuracy.Simulations are performed using the modified IEEE 33-node system.A typical time period is selected to analyze the node voltage variation,and the results show that the maximum voltage deviation can be reduced from 14.06%to 4.54%.The maximum peak-valley difference of the system can be reduced from 8.83 to 4.23 MW,and the voltage qualification rate can be significantly improved.Moreover,the validity of the proposed model is verified through simulations.

Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles

Managing the charging process of a large number of electric vehicles to decrease the pressure on the local electricity grid is of high interest to the utilities. Using efficient mathematical optimization techniques, the charging behavior of electric vehicles shall be optimally controlled taking into account network, vehicle, and customer requirements. We developed an efficient algorithm for calculating load shift potentials defined as the range of all charging curves meeting the customer’s requirements and respecting all individual charging and discharging constraints over time. In addition, we formulated a mixed integer linear program (MIP) applying semi-continuous variables to find cost-optimal load curves for every vehicle participating in a load shift. This problem can be solved by e.g. branch-and-bound algorithms. Results of two scenarios of Germany in 2015 and 2030 based on mobility studies show that the load shifting potential of EV is significant and contribute to a necessary relaxation of the future grid. The maximum charging and discharging power and the average battery capacity are crucial to the overall load shift potential.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Future evolution of automated demand response system in smart grid for low-carbon economy

Smart grid construction is an important carrier and an effective way to promote the development of lowcarbon economy.Demand response(DR)is commonly regarded as an important core technology in smart grid field,and it reflects the flexible and interactive features of the core business in smart electricity.It is the developing direction of automated demand response(ADR)technology,and its main features are the standardization of information exchange,together with the intelligence of decision-making and the automation of implementations.ADR technology can improve the efficiency of the whole power system and enhance the ability to accept new energy sources.This paper analyzes the role of demand response in improving efficiency and low-carbon energy saving power systems.The automated demand response system architecture is investigated,and the ADR roadmap of commercial/industrial and residential customer is proposed.The key technologies for ADR system are analyzed,including demand response strategy,information exchanging model,measurement and verification techniques,and multi-agent scheduling techniques.To ensure the interoperability between the grid side and the user side,the ADR business in smart grid user interface standards is concluded to support further demand side management project.

Experimental study on the new type of electrical storage heater based on flat micro-heat pipe arrays

A new type of electrical storage heater that utilizes latent heat storage and flat micro-heat pipe arrays （FMHPAs） was developed. The thermal characteristics of the heater were tested through experimentation. The structure and operating principle of the storage heater were expounded. Three rows of FMHPAs were applied （three rows with five assemblies each） with a mass of 28 kg of phase change material （PCM） in the heat storage tank. Electric power was supplied to the PCM in the range of 0.2-2.04 kW, and air was used as heat transfer fluid, with the volume flow rate ranging from 40-120 m3/h. The inlet temperature was in the range of 15-24~C. The effects of heating power, air volume flow rate, and inlet temperature were investigated. The electrical storage heater exhibited efficiencies of 97% and 87% with 1.98 and 1.30 kW of power during charging and discharging, respectively. Application of the proposed storage heater can transfer electricity from peak periods to off-peak periods, and the excess energy generated by wind farms can be stored as heat and released when needed. Good economic and environmental benefits can be obtained.