Distributed Energy Management Of Smart Grids Based On Randomized Gradient-Free Optimization Algorithm

At present,the increasing global population leads to the increasing demand for energy.Under the background of the rapid development of renewable energy,this paper studies the energy management of smart grids.Most experts and scholars at home and abroad only focus on the economic dispatch of the smart grid,or most of the power generation side only consider the cost function in the form of quadratic function.In addition,there are a lot of disadvantages in the centralized energy management of the smart grid and the communication burden between nodes in the smart grid is too heavy.Therefore,in view of the shortcomings of the existing literature,this paper considers the profit function of power generation side and load side at the same time,establishes the cost function of power generation side as a nonconvex and nondifferentiable form with valve point effect,transforms the centralized energy management into distributed form to reduce the computational burden,and adds event-triggering mechanism to reduce the communication burden.In order to provide an optimization algorithm for the energy management of the smart grid,a distributed event-triggered randomized gradient-free optimization algorithm is proposed.The convergence of the algorithm is guaranteed by setting the non-additive,square-summable and decreasing stepsize,and the event-triggering mechanism is added to reduce the burden of network communication.In order to solve the distributed optimization problem of minimizing the sum of local non-smooth convex objective functions composed of several agents,a distributed event-triggered randomized gradient-free optimization method is proposed,and its consensus and optimality are strictly analyzed.It is proved that the algorithm can finally make the states of each agent converge to the average and optimal value.Finally,the simulation results show that the algorithm can greatly reduce the number of communication times while achieving good convergence and optimal performance.Then,consider the distributed energy management of the smart grid with valve-point effect.Because the cost function of power generation unit is nonconvex and nondifferentiable,a randomized gradient-free method based on successive convex approximation is proposed,which uses a convex smooth surrogate function to replace the cost function,providing a more accurate objective function for the global optimal solution.In order to reduce the burden of computation and communication,a distributed optimization algorithm based on event-triggered communication is designed to optimize the energy management of the smart grid,and the convergence of the algorithm is strictly proved.Eventually,the simulation analysis is carried out under the IEEE 9 bus system.It is found that the algorithm can achieve excellent convergence effect and greatly reduce the numbers of communication between nodes.Compared with the consensus based energy management algorithm(CEMA)without considering the valve-point effect in the power generation cost function,the algorithm can obtain better social welfare.In order to illustrate the scalability the algorithm,it is verified that it can also obtain good convergence effect and reduce the communication burden under IEEE 39 bus system.Compared with the consensus based energy management algorithm(CEMA)without considering the valve-point effect in the power generation cost function,it can also obtain better social welfare.