Research On Strategy Of Load Demand Response Based On Multi-Energy Complement

As environmental and energy problems have been increasingly addressed,the application of clean energy is becoming an inevitable trend.However,most of the new energies such as wind power and photovoltaic are restricted by natural conditions,and they have shortcomings including low energy density,unreliable and discontinuous power supply.As demand response technology can manage and control demand side of power load,the effective interaction between power load and power supply and clean energy full accommodation could be realized.Designing the appropriate electricity pricing mechanism is the most important part of demand response,and the effect of the demand response would be affected directly.Taking full account of influencing factors including price strategy,price incentive regulation,user demand price elasticity,etc.,the demand response theoretical model is deduced based on the benefit maximization model,and then the simulation example is used to prove the correctness and validity of the mathematical model.Meanwhile,mathematical model is used to simulate calculation in case of the combination of different parameter conditions,and the results show that there are positive correlation between users' initiative to participate demand response and peak valley price fluctuations of TOU power price and Real-time price,incentive price based on demand response,and users'demand price elasticity respectively;raising incentive price can directly stimulate more users to participate in demand response.Demand response price optimization strategy based on Particle Swarm Optimization(PSO)algorithm is studied to develop a reasonable price mechanism and achieve effect of power peak load shifting.The PSO algorithm is innovatively applied to optimize electricity price strategy,and fitness function of the average price and demand response effect is proposed by building the mapping relationship between the particles in the particle swarm and electricity price,and then optimal TOU power price and Real-time price are calculated by proposing iteration steps.Finally,the effectiveness and quick convergence of the PSO algorithm is verified by the results of simulation example.The PSO algorithm based on spline approximation is also proposed to solve the problem of too many parameters during the optimization process of Real-time price,which quickens the convergence speed,reduces the computational complexity and promotes optimize efficiency significantly.Aimed to deal with the new energy accommodation problem,the so-called Difference-Degree Theory is used between the electricity supply curve and electricity load curve.Furthermore,the difference-degree function between both curves is defined as the evaluation criterion of their matching degree.Using this theory,various kinds of typical energies,such as wind-generated electricity and photovoltaic power,are taken into computation,which suggests that new energy has higher difference-degree than traditional ones.It also shows that multi-energy complement is able to achieve better difference-degree.Under this multi-energy complement,the PSO algorithm based real-time electricity price optimal strategy is introduced into demand response,where the original load curve will constantly approach the electric power supply curve generated by new energies.Thus,computation results show that energy accommodation can be achieved with high efficiency.