CN108512238A - Smart home two benches Optimization Scheduling based on Demand Side Response - Google Patents

Abstract

The present invention relates to the smart home two benches Optimization Schedulings based on Demand Side Response to establish spatial load forecasting model and energy storage Controlling model for the intelligent domestic system containing energy storage device, and two benches Optimized Operation is carried out to intelligent appliance.First stage with flexible load object in order to control, is solved using genetic algorithm；Second stage is with energy storage device object in order to control, using PSO Algorithm.The optimal solution of first stage optimal control participates in second stage optimal control in the form of basic load.The fitness value of spatial load forecasting stage optimal solution controls the minimum target function constraint in stage as energy storage, to further decrease the electric cost of electric network terminal user.Consider the peak load shifting characteristic of the Demand Side Response based on market guidance, smart home scheduling forms a kind of benign relation between supply and demand for grid sources lotus to have great significance.

Description

Two-stage optimal scheduling method for smart home based on demand-side response

technical field

The invention provides an economical optimal scheduling method for the electricity consumption of the smart home system.

Background technique

With the consumption of fossil fuels, the rapid development of human society has increasingly tense demand for energy, and the contradiction between human development and the natural environment has triggered a series of green and new energy exploration booms. At the beginning of the 21st century, photovoltaics, wind power, hydropower, and nuclear power have become key industries in many countries' energy revolution strategies. It is estimated that by 2050, the proportion of non-fossil energy in the world will exceed 50%. Due to the government's policy support, the photovoltaic industry has developed rapidly, and the number of home-installed photovoltaic panels has also increased year by year. With the maturity of energy storage technology, new concepts such as electric vehicles have already been deeply rooted in the hearts of the people. The combined power generation system composed of photovoltaic panels and energy storage devices is widely used in small residential courtyards and rural areas, and has been widely promoted in European and American countries.

Smart home dispatch is based on the demand side response of the power grid terminal with the market electricity price as the reference signal. Optimal scheduling of smart home appliances refers to controlling the use of home appliances in terms of time or power under the condition of satisfying basic conditions, so as to achieve the goal of minimizing operating costs or load fluctuations, thereby reducing user electricity costs or reducing grid peak hours. load pressure. In recent years, the main problem faced by smart home dispatching is the measurement and detection automation technology. Due to the slow development of this technology in power terminals, it is difficult to carry out terminal smart power projects. With the gradual formation and improvement of the advanced metering system ^[7] , the detection of the operating status of smart home appliances and the systemic realization of the start-stop control function make it possible to respond to the demand side of smart appliances.

Scholars at home and abroad have few research results on smart home system scheduling. From the consumer's point of view, the grid terminal intelligent power utilization technology considers the characteristics of energy storage and load, and proposes a two-stage coordinated operation strategy for smart home appliances to meet the needs of users for electricity consumption and satisfaction with electricity consumption.

Contents of the invention

Optimal scheduling of smart home appliances refers to controlling the use of home appliances in terms of time or power under the condition of satisfying basic conditions, so as to achieve the goal of minimizing operating costs or load fluctuations, thereby reducing user electricity costs or reducing grid peak hours. load pressure. In recent years, the main problem faced by smart home dispatching is the measurement and detection automation technology. Due to the slow development of this technology in power terminals, it is difficult to carry out terminal smart power projects. With the gradual formation and improvement of the advanced metering system ^[7] , the detection of the operating status of smart home appliances and the systemic realization of the start-stop control function make it possible to respond to the demand side of smart appliances.

The difficulty of smart home energy storage dispatching technology lies in the uncertainty of prediction, load and the randomness and volatility of new energy power generation. On the premise that the user recognizes the dispatching results of the load control algorithm, the load curve within the dispatching period becomes a known quantity, and the central controller of the home intelligent dispatching system will no longer consider the load characteristics of the demand side. On this basis, the research object is future Households that install photovoltaic panels or small fans, that is, power users without micro-power sources, combined with the above two points, home energy storage dispatching will fully get rid of the influence of prediction accuracy. Considering that the energy storage system has the characteristics of buffering electric energy to stabilize load fluctuations and uninterrupted power supply under emergency faults, the optimal dispatch of smart home systems with energy storage will benefit the energy distribution and buffering of the grid, as well as the emergency response of users under faults.

From the perspective of the distribution network, the present invention chooses to reduce the flexible load power consumption or switch to the energy storage system for power supply when the grid load peak releases a high electricity price signal, which alleviates the power supply pressure of the grid, and chooses to increase the electricity consumption when the grid load is low and releases a low electricity price signal. Using electricity or charging the energy storage system will not cause waste of resources.

The present invention adopts following technical scheme to carry out:

The two-stage optimal scheduling method for smart home based on demand-side response is characterized in that, based on the establishment of a load control model and an energy storage control model, two-stage optimal scheduling is performed on smart home appliances, including:

Step 1. In the first stage, the flexible load is used as the control object, and the genetic algorithm is used to solve the problem;

Step 2. The optimal solution of the first-stage optimal control participates in the second-stage optimal control in the form of base load. In the second stage, the energy storage device is used as the control object, and the particle swarm algorithm is used to solve the problem. The fitness value of the optimal solution in the load control stage is used as the minimum objective function constraint in the energy storage control stage, so as to further reduce the power consumption cost of the grid end users.

In the above-mentioned two-stage optimal scheduling method for smart homes based on demand-side response, the first stage aims to consider the positive benefit function of user experience in electricity consumption, and the expression is as follows:

F ₁ ＝max(f ₁ -f ₂ ) Formula 6

Among them, f ₁ is user satisfaction, f ₂ is electricity cost.

In the system operation period T, the user's power consumption satisfaction and power consumption are expressed as follows:

In the formula, c _k is the electricity price level in the k period, and p _k is the electricity consumption in the k period.

The battery model is as follows

Battery remaining energy storage expression

E _b,t ＝SOC _t *C Formula 10

In the formula, SOC _t is the state of charge of the battery at time t, and is the ratio of the remaining energy storage E _b,t of the battery to the rated capacity C; μ _c and μ _d are the charging and discharging efficiencies of the battery respectively; the remaining energy storage E _{b,t of} the battery and the unit of rated capacity C is kWh.

In the above-mentioned two-stage optimal scheduling method for smart home based on demand side response, the load control in the first stage is solved by genetic algorithm, which specifically includes:

Step 3.1. Genetic algorithm parameter settings for load control in the first stage: population size, solution space, zero iteration times, and iteration termination conditions.

Step 3.2. Initialize all chromosome groups in the solution space: initialize the running time or power of the smart home appliance according to the characteristics of the flexible load.

Step 3.3: Keep the chromosome with the highest fitness in each group of chromosomes, merge into a new chromosome group and directly participate in the next iteration, and the rest of the chromosomes will perform crossover and mutation operations in the second stage.

Step 3.4, judge whether the iteration termination condition is satisfied, if so, exit the first stage of load optimization, and proceed to the second stage of energy storage optimization, and use the optimal solution of the first stage as the algorithm start condition of the next stage; otherwise, the non-optimal Perform crossover or mutation operations on the optimal chromosome set, and proceed to the next iteration.

Step 3.5. Repeat the above steps until the iteration termination condition is met, and enter the next stage of energy storage optimization.

In the above-mentioned smart home two-stage optimal scheduling method based on demand-side response, considering the power characteristics of the energy storage system, the operating cost of the smart home will consist of two parts: electricity purchase fees and electricity sales revenue. When the energy storage discharge power is higher than the actual load demand, the system feeds power to the grid and charges a certain fee. This part of the fee is the electricity sales revenue on the user side. The optimization goal of the second stage is to minimize the operating cost of the system in the grid-connected mode, and the mathematical expression is as follows:

F ₂ =min(f _g -f _s )

Formula 11

in

In the formula, f _g , f _s are the user's electricity purchase cost and the user's electricity sales revenue; C _g,k , C _s,k are the electricity purchase price and electricity sale price at time k respectively; P _g,k , P _s,k They are the power purchased by the user and the power sold by the user at time k.

The operating cost of the optimized system in the second stage shall not be higher than that in the first stage,

F ₂ <f ₂ formula 16

In the formula, F ₂ is the optimization objective of the second stage, and f ₂ is the minimum operating cost of the system in the first stage of load control.

In order to prevent the battery from being overcharged or overdischarged to prolong its service life, the state of charge of the battery is limited during the energy storage optimization control process:

SOC _min ≤ SOC _t ≤ SOC _max Formula 17

Considering the impact of excessive charging and discharging power on the battery capacity, this paper restricts the charging power per unit time of the battery.

The cumulative discharge capacity is an index reflecting the life of the battery. A low daily cumulative discharge is beneficial to prolong the working life of the battery, but it limits the transfer of electric energy from the battery, which is not conducive to the economic operation of the system; if the daily cumulative discharge is too high, it is helpful for flexible scheduling, but it will affect the service life of the battery. Therefore, to establish the accumulative discharge constraint of the battery,

In the formula, E _dT is the accumulative daily discharge capacity of the battery; They are the upper and lower limits of the accumulated daily discharge of the battery, if the daily discharge of the battery must not exceed the upper and lower limits.

In the above-mentioned two-stage optimal scheduling method for smart home based on demand-side response, the energy storage control in the second stage is solved by particle swarm algorithm, which specifically includes:

Step 5.1, parameter setting of particle swarm algorithm for energy storage control in the second stage: solution space constraints, number of iterations, number of particles, and termination conditions of iterations.

Step 5.2. Initialize the particle swarm: N different vectors are randomly generated in the 24-dimensional solution space vector satisfying the model constraints, and each vector corresponds to a particle to form the initial particle swarm, and start the loop iteration.

Step 5.3. Select local optimization or global optimization by judging the number of iterations: when the number of iterations is lower than the set value, enter local optimization; when the number of iterations is equal to or greater than the set value, perform global optimization.

Step 5.4. According to the load value at each moment and the power information carried by the particles, determine the amount of electricity purchased by the user and calculate the fitness function value.

Step 5.5. Select the optimal particle of the group and the optimal position of the individual history, update the solution space position and moving speed of the particle swarm, and form a new particle swarm

Step 5.6. Determine whether the termination condition is satisfied, and if so, end the second-stage optimization process, and arrange the scheduling control plan according to the optimal solution; otherwise, repeat the energy storage optimization step and continue to the next iteration.

The invention considers the peak-shaving and valley-filling characteristics of the demand side response based on the market electricity price, and the smart home dispatching is of great significance for the power grid source and load to form a benign supply-demand relationship.

Description of drawings

Figure 1 is a flow chart of the two-stage algorithm.

Figure 2 is a structural diagram of the smart home system.

Specific implementation steps

The embodiments of the present invention will be further described below in conjunction with the accompanying drawings. The control structure of the present invention includes a dispatch center, multiple data collectors and energy storage system controllers. The controller and each data collector perform two-way signal transmission, and each data The collectors cannot communicate with each other, the dispatch center processes the data collection signals to optimize the energy storage plan, and the smart home system charges and discharges according to the instructions of the controller.

According to the rated power of each flexible load and the user's electricity consumption habits, the benchmark and boundary reference quantity of each uncertain quantity can be obtained. For the power of each household appliance flexible load operating state, such as the base value The upper and lower bounds of the fluctuation range of the disturbance ε _k and the total energy consumption in a working cycle T and W _k , etc.; for the running time of each household appliance load, such as the running time period [α _k , β _k ], the starting time of running end run time Runtime length d _k etc. Express the above parameters as an uncertain set:

In the formula Indicates the operating status of the load during the k period. If the load is running in the k period, the value is 1, and if the load is not running, the value is 0.

The first stage of smart home scheduling aims to consider the positive benefit function of user experience in electricity consumption, and the expression is as follows:

F ₁ =max(f ₁ -f ₂ ) (6)

Among them, f ₁ is user satisfaction, f ₂ is electricity cost.

In the system operation period T, the user's power consumption satisfaction and power consumption are expressed as follows:

In the formula, c _k is the electricity price level in the k period, and p _k is the electricity consumption in the k period.

The battery model is as follows

Battery remaining energy storage expression

E _b,t =SOC _t *C (10)

In the formula, SOC _t is the state of charge of the battery at time t, and is the ratio of the remaining energy storage E _b,t of the battery to the rated capacity C; μ _c and μ _d are the charging and discharging efficiencies of the battery respectively; the remaining energy storage E _{b,t of} the battery and the unit of rated capacity C is kWh.

Considering the power characteristics of the energy storage system, the operating cost of a smart home will consist of two parts: electricity purchase fees and electricity sales revenue. When the energy storage discharge power is higher than the actual load demand, the system feeds power to the grid and charges a certain fee. This part of the fee is the electricity sales revenue on the user side. The optimization goal of the second stage is to minimize the operating cost of the system in the grid-connected mode, and the mathematical expression is as follows:

F ₂ ＝min(f _g -f _s ) (11)

in

In the formula, f _g , f _s are the user's electricity purchase cost and the user's electricity sales revenue; C _g,k , C _s,k are the electricity purchase price and electricity sale price at time k respectively; P _g,k , P _s,k They are the power purchased by the user and the power sold by the user at time k.

The operating cost of the optimized system in the second stage shall not be higher than that in the first stage,

F ₂ < f ₂ (16)

In the formula, F ₂ is the optimization objective of the second stage, and f ₂ is the minimum operating cost of the system in the first stage of load control.

In order to prevent the battery from being overcharged or overdischarged to prolong its service life, the state of charge of the battery is limited during the energy storage optimization control process:

SOC _min ≤ SOC _t ≤ SOC _max (17)

Considering the impact of excessive charging and discharging power on the battery capacity, this paper restricts the charging power per unit time of the battery.

The cumulative discharge capacity is an index reflecting the life of the battery. A low daily cumulative discharge is beneficial to prolong the working life of the battery, but it limits the transfer of electric energy from the battery, which is not conducive to the economic operation of the system; if the daily cumulative discharge is too high, it is helpful for flexible scheduling, but it will affect the service life of the battery. Therefore, to establish the accumulative discharge constraint of the battery,

In the formula, E _dT is the accumulative daily discharge capacity of the battery; They are the upper and lower limits of the cumulative daily discharge of the battery, if the daily discharge of the battery must not exceed the upper and lower limits.

The first stage load control is solved by genetic algorithm; the second stage energy storage control is solved by particle swarm algorithm.

The reason for choosing the genetic algorithm in the first stage is that the genetic algorithm is suitable for solving discrete problems. The load control model in the first stage of this paper belongs to the discrete-continuous model. The equipment state is represented by 0 and 1, which is a discrete mathematical expression, and the power Controlled load is a continuous factor in the model. Considering the full-time operation characteristics of power controllable load, the continuous factor and discrete factor in the model are independent of each other, and the continuous power controllable load can be distributed and processed, discrete-continuous separation processing The method has no effect on the control effect.

The reason for choosing the particle swarm optimization algorithm in the second stage is that the particle swarm optimization algorithm is mainly used to solve complex nonlinear optimization problems. A two-stage optimization method is adopted. The global search in the first stage determines the approximate position of the optimal solution, and the local search in the second stage locks the optimal solution. solution, the algorithm has high solution efficiency and is suitable for optimization of the solution space in the real number field. In the second stage of this paper, a scheduling control plan is formulated for the charging and discharging power of the energy storage device in the full-time domain. The value range of the controlled quantity is a continuous real number field. The energy storage scheduling optimization problem in this paper can be known from the model constraints and objective functions of the second stage. It is a continuous nonlinear non-deterministic solution problem, the global optimization method can be strengthened in the optimization parameter setting of the particle swarm optimization algorithm to avoid falling into the local optimum, and the global optimal solution can be obtained with a high probability.

The entire method flow is shown in Figure 1.

The data acquisition uploads the input signal of the system to the controller. After the controller receives each data acquisition signal, it judges whether there is an input signal exceeding the preset range. If not, no operation is performed. If there is a signal exceeding the preset range, Send a confirmation signal to the data acquisition, if the new signal appears within the allowable delay time, the signal will be judged in the preset interval, otherwise, the information communication will be terminated. The dispatch center sends the dispatch plan to the controller to control the economic operation of the smart home system.

The algorithm flow chart is shown in Figure 2, and the specific implementation steps are as follows:

1) Genetic algorithm parameter settings for load control in the first stage: population size, solution space, zero iteration times, and iteration termination conditions.

2) Initialize all chromosome groups in the solution space: initialize the running time or operating power of smart home appliances according to the flexible load characteristics.

3) The chromosome with the highest fitness is retained in each set of chromosomes, and merged into a new chromosome set to directly participate in the next iteration, and the rest of the chromosomes will be crossed and mutated in the next step.

4) Judging whether the iteration termination condition is satisfied, if so, exit the first stage of load optimization, and proceed to the second stage of energy storage optimization, and use the optimal solution of the first stage as the algorithm start condition for the next stage; otherwise, the non-optimal The chromosome set performs crossover or mutation operations and continues to the next iteration.

5) Repeat the above steps until the iteration termination condition is met, and enter the next stage of energy storage optimization.

6) The parameter settings of particle swarm algorithm for energy storage control in the second stage: solution space constraints, number of iterations, number of particles, and termination conditions of iterations.

7) Initialize the particle swarm: N different vectors are randomly generated in the 24-dimensional solution space vector satisfying the model constraints, and each vector corresponds to a particle to form the initial particle swarm, and the loop iteration starts.

8) Select local optimization or global optimization by judging the number of iterations: when the number of iterations is lower than the set value, enter local optimization; when the number of iterations is equal to or greater than the set value, perform global optimization.

9) According to the load value at each moment and the power information carried by the particles, determine the amount of electricity purchased by the user and calculate the fitness function value.

10) Select the optimal particle of the swarm and the optimal position of the individual history, update the solution space position and moving speed of the particle swarm, and form a new particle swarm

11) Determine whether the termination condition is satisfied, if so, end the second-stage optimization process, and arrange the scheduling control plan according to the optimal solution; otherwise, repeat the energy storage optimization step and continue to the next iteration.

Parameter determination:

1) According to the minimum running time constraints of relevant smart home appliances, this paper stipulates that the load control time slot is 15 minutes.

2) The power of the flexible load remains unchanged in the standard time slot, and it can only be in one of the operating or non-operating states during this period.

3) Assuming that the operating power of all loads remains unchanged in each standard time interval, the total user load is the accumulation of power in each period.

4) The simulation scene is set in a family residence in a residential area. The household has no photovoltaic installed capacity, and the energy storage device is a lithium battery with a capacity of 20KW.

5) Considering the impact of the life of the lithium battery, the initial state of charge and the final state of charge of the energy storage device are both set to 40%.

The embodiments of the present invention are illustrative rather than restrictive, so the invention is not limited to the embodiments described in the specific implementation, any other implementations obtained by those skilled in the art according to the technical solution of the present invention, Also belong to the protection scope of the present invention.

Claims (5)

1. the smart home two benches Optimization Scheduling based on Demand Side Response, which is characterized in that based on establishing spatial load forecasting Model and energy storage Controlling model carry out two benches Optimized Operation to intelligent appliance, including：

Step 1, first stage with flexible load object in order to control, are solved using genetic algorithm；

Step 2, the optimal solution of first stage optimal control participate in second stage optimal control in the form of basic load, and second Stage with energy storage device object in order to control, using PSO Algorithm；The fitness value conduct of spatial load forecasting stage optimal solution Energy storage controls the minimum target function constraint in stage, to further decrease the electric cost of electric network terminal user.

2. the smart home two benches Optimization Scheduling based on Demand Side Response as described in claim 1, it is characterised in that： First stage using consider user power utilization experience positive benefit function as target, expression formula is as follows：

F₁=max (f₁-f₂) formula 6

Wherein, f₁It is user satisfaction, f₂It is electricity cost；

In system operation cycle T, the satisfaction of custom power consumption and electricity consumption spend and indicate as follows respectively：

In formula, c_kIt is the electricity price level of k periods, p_kIt is the electricity consumption of k periods；

Battery model is as follows

Accumulator residue energy storage expression formula

E_b,t=SOC_t* C formulas 10

In formula, SOC_tIt is the state-of-charge of t moment accumulator, is accumulator residue energy storage E_b,tWith the ratio of rated capacity C；μ_c、 μ_dIt is accumulator cell charging and discharging efficiency respectively；Accumulator residue energy storage E_b,tUnit with rated capacity C is kilowatt hour.

3. the smart home two benches Optimization Scheduling based on Demand Side Response as claimed in claim 2, it is characterised in that： First stage spatial load forecasting is solved using genetic algorithm, is specifically included：

The genetic algorithm parameter setting of step 3.1, first stage spatial load forecasting：Population scale, solution space, iterations zero, Stopping criterion for iteration；

Step 3.2 initializes all genomes in solution space：Intelligent appliance is initialized according to flexible load characteristic Run time or operation power；

Step 3.3 retains the highest chromosome of fitness in every group chromosome, is merged into new genome and participates directly in Next iteration, remaining chromosome will carry out intersection and mutation operation in second stage；

Step 3.4 judges whether to meet stopping criterion for iteration, if then exiting first stage load optimal, carries out second stage Energy storage optimizes, and using the optimal solution of first stage as the algorithm entry condition of next stage；If otherwise to non-optimal chromosome Group carries out intersection or mutation operation, continues next iteration.

Step 3.5 repeats above step, and until meeting stopping criterion for iteration, the energy storage into next stage optimizes.

4. the smart home two benches Optimization Scheduling based on Demand Side Response as described in claim 1, it is characterised in that： Consider that the power supply characteristic of energy-storage system, the operating cost of smart home will consist of two parts：Power purchase expense and power selling income； When energy storage discharge power is higher than actual load demand, system feeds to power grid and collects certain expense, this part expense is used The power selling income of family side；The optimization aim of second stage is that system operation cost minimizes under grid-connect mode, and mathematic(al) representation is such as Under：

F₂=min (f_g-f_s) formula 11

Wherein

In formula, f_g、f_sIt is that user's power purchase is spent respectively, user's sale of electricity income；C_g,k、C_s,kIt is k moment purchase electricity price, sale of electricity respectively Electricity price；P_g,k、P_s,kIt is k moment user power purchases power, user's sale of electricity power respectively；

System operation cost after second stage optimization must not be higher than the system operation cost of first stage,

F₂＜ f₂Formula 16

In formula, F₂It is the optimization aim of second stage, f₂It is the system minimum operating cost of first stage spatial load forecasting；

State is put to prolong the service life in order to avoid accumulator is in overcharge or cross, and is limited and is stored during energy storage optimal control Battery charge state：

SOC_min≤SOC_t≤SOC_maxFormula 17

Consider the excessive influence accumulator capacity of charge-discharge electric power, the charge power in battery cell's time is subject to about herein Beam；

Accumulated discharge amount is the index of a reflection life of storage battery；Day accumulated discharge amount is low to be beneficial to extend the battery-operated longevity Life, but accumulator transfer electrical energy is limited, it is unfavorable for the economical operation of system；If day, accumulated discharge amount was excessively high, contribute to flexibly Scheduling, but the service life of accumulator can be influenced；Therefore, the constraint of accumulator accumulated discharge is established,

In formula, E_dTFor accumulator day accumulated discharge amount；It is the bound of accumulator day accumulated discharge amount respectively, if Accumulator day, discharge capacity must not exceed bound restriction range.

5. the smart home two benches Optimization Scheduling based on Demand Side Response as claimed in claim 4, it is characterised in that： Second stage energy storage control uses PSO Algorithm, specifically includes：

Step 5.1, the particle cluster algorithm parameter setting of second stage energy storage control：Solution space constraint, iterations, population Mesh, stopping criterion for iteration；

Step 5.2, initialization population：N number of difference is randomly generated in the 24 dimension solution space vectors for meeting model constraints Vector, each corresponding particle of vector constitutes primary group, starts the cycle over iteration；

Step 5.3, by judging that iterations select local optimum or global optimization：When iterations be less than setting value, into Enter local optimum；When iterations are equal to or more than setting value, progress global optimization；

Step 5.4, the power information entrained by each moment load value and particle, determine user's purchase of electricity size and calculate Fitness function value；

Step 5.5, selection group's optimal particle and the optimal position of individual history, update solution space position and the movement of population Speed constitutes new population

Step 5.6 judges whether to meet end condition, if then terminating two stage optimization process, dispatches and controls by optimal solution arrangement System plan；Otherwise energy storage Optimization Steps are repeated, next iteration is continued.