CN110705921A - Industrial user energy consumption assessment method based on hybrid power generation system and demand response - Google Patents
Industrial user energy consumption assessment method based on hybrid power generation system and demand response Download PDFInfo
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Abstract
A hybrid power generation system and demand response based industrial user energy consumption assessment method belongs to the technical field of power systems. The invention aims to provide a hybrid power generation system and a demand response industrial user energy consumption evaluation method which are designed aiming at the demands of industrial users through comprehensive multiple data evaluation. The method comprises the following steps: modeling the energy classification provided for industrial customers in different modes; determining a power balance constraint and a bilateral contract constraint; evaluating the energy production of the internal system and the energy storage system; evaluating the generated energy of the wind power and solar energy systems; evaluating participation demand response energy consumption; and providing a solving algorithm and an optimizing method. The purchasing cost of the invention is quantitatively researched, which provides help for the construction of the power Internet of things and has practical application value.
Description
Technical Field
The invention belongs to the technical field of power systems.
Background
With the development of social economy and the improvement of productivity, the global energy consumption is rapidly increased, and the electric energy consumption of industrial users accounts for more than the energy consumption of residents in the whole society. Meanwhile, with the gradual increase of the industrialization level and the rise of distributed new energy power generation, the energy acquisition mode of industrial users is changed from the traditional single power market. Many industrial users now have access to energy from different sources, including the electricity market, reciprocal agreements, self-contained small turbines, energy storage systems, new energy generation (e.g., wind, solar) and as demand response plan (DeRP) participants.
Demand Response (Demand Response), or simply called power Demand Response, means that when the wholesale market price of power increases or the reliability of the system is threatened, after a power consumer receives a direct compensation notification of an inductive load reduction or a power price increase signal sent by a power supplier, the power consumer changes its inherent conventional power consumption mode to reduce or shift the power consumption load for a certain period of time to respond to power supply, so as to ensure the stability of the power grid and suppress the short-term behavior of power price increase. It is one of the solutions for Demand Side Management (DSM). The price-based demand response means that the user adjusts the power demand accordingly according to the received price signal, including Time of Use Pricing (TOU), Real Time Pricing (RTP), peak Pricing (CPP), and the like. The schedulable resources primarily employ incentive based demand side response policies. Unlike demand side resources, which refer to the temporal change in demand side resource availability in response to power market or grid reliability, demand side responses refer to measurable, concentrated loads that can provide demand side responses.
The utilization of new energy for power generation has been widely concerned by numerous scholars at home and abroad, and China encourages industrial users to obtain energy supply through clean energy, and large consumers of electricity hope to increase the energy utilization rate by participating in a demand response plan. However, the method for evaluating energy usage based on consideration of major users of photovoltaic systems, wind turbines, Energy Storage Systems (ESS) and DeRP has not been widely studied quantitatively, which is a short board of the internet of things for electric power. At present, research and consideration of demand response are based on the supply and demand interaction angle, and the influence of participation in a demand response plan is not considered while new energy consumption is considered. Therefore, an energy consumption evaluation method for determining that an industrial user uses the energy storage system simultaneously based on a hybrid power generation system and demand response and comprehensively considering supply and demand interaction and multi-energy complementation is sought, and the method has practical application value.
Disclosure of Invention
The invention aims to provide a hybrid power generation system and a demand response industrial user energy consumption evaluation method which are designed aiming at the demands of industrial users through comprehensive multiple data evaluation.
The method comprises the following steps:
step 2, determining power balance constraint and bilateral contract limit;
step 3, evaluating the energy production of the internal system and the energy storage system;
step 4, evaluating the generated energy of the wind power and solar energy system;
step 5, evaluating the energy consumption for participating in demand response;
and 6, providing a solving algorithm and an optimizing method.
The invention provides a solution to the problem of energy cost calculation for industrial customers and studies the influence of the demand response problem on the overall cost of the industrial customers. Furthermore, in order to take into account the conventional and worst case of the total cost of the industrial customer, an efficient method of generating electricity taking into account the integrated energy is proposed. Based on the consideration of the electricity purchase cost of main users of a photovoltaic system, a wind turbine, an Energy Storage System (ESS) and a DeRP, the quantitative research is carried out, the assistance is provided for the construction of a model in the electricity Internet of things, and the method has the value of practical application.
Drawings
FIG. 1 is a metric space model of a binocular case example;
FIG. 2 is a collection of evenly spaced points on an optimal line for a dual target problem;
FIG. 3 is an application of a conventional constraint method to a dual target problem;
FIG. 4 is an optimal hyperplane for a tri-objective function;
FIG. 5 is a view of evenly spaced points on the optimal plane;
FIG. 6 is a pareto filter;
fig. 7 is a pareto filter flow diagram.
Detailed Description
The method comprises the following steps:
Step 2, determining power balance constraint and bilateral contract constraint: the power balance limit is represented by an equation. The amount of energy purchased by pool markets, reciprocal protocols and wind power, solar panels, MT and energy storage should be the same as the extra load present by the DeRP and the required energy needs to be retained in the storage system.
Bilateral contract restrictions: the constraints on the reciprocal protocol are expressed in equations. The power range of the reciprocal protocol is expressed in terms of equations. Further, the total purchase energy from the reciprocal protocol is expressed in terms of an equation.
Step 3, modeling an internal system and an energy storage system:
to represent the operating cost of the microturbine, a three-stage linear technique is employed. Representing the operating costs of the microturbine. The equation represents the magnitude of the power limit in each section. Further, the maximum power in the first segment is indicated. A technical feature is represented which represents the speed of rise and fall of the generated energy as rise and fall constraints. Calculating the minimum rising and falling time used in the two Up and Dn time auxiliary variables shown in the above equation by using a formula;
the energy storage system limit is expressed in terms of an equation. Representing the energy stored in the battery prior to calculation. Representing the highest possible charge and discharge energy of the battery. The equation represents the range of energy retained. The equality constraint prevents the storage system from being charged and discharged at the same time. Finally, the equation represents the calculation of the reserve energy in the battery at any given time.
Step 4, modeling wind power and solar energy:
wind power generation is calculated for a given time, limiting the energy usage generated by the wind turbine.
And (3) modeling solar power generation, namely utilizing solar panels to generate electric energy by utilizing solar energy. Furthermore, the maximum energy produced from this type is generally assumed. And calculating the solar power generation in a given time and calculating the maximum energy yield. The energy used by the solar panel is limited.
Step 5 modeling the demand response plan:
the TOU change of the DeRP is realized in the step. DeRP smoothes load distribution and reduces operating costs by moving energy consumption from peak times to other times. With DeRP, a portion of the consumption can be moved from peak hours to other times. The consumption pattern generated using DeRP can be calculated by an equation.
Furthermore, the technical limitations of DeRP are expressed in equations.
Step 6 provides a solving algorithm and an evaluation method:
an evaluation method is provided by using a pareto filter algorithm, and detailed steps are shown in the attached drawings.
The method comprises the following specific steps:
step 2, determining power balance constraint and bilateral contract constraint: the power balance limit is represented by equation (2). The formula shows that the amount of energy purchased by pool markets, reciprocal protocols and wind power, solar panels, MT and energy storage should be the same as the extra load present by the DeRP and the required energy needs to be retained in the storage system.
Bilateral contract restrictions:
equations (3) and (4) represent the constraints on the reciprocal protocol. Equation (3) represents the power range of the reciprocal protocol. Further, equation (4) represents the total purchase energy from the reciprocal protocol.
Step 3, modeling an internal system and an energy storage system:
to represent the operating cost of a microturbine, a three-stage linear technique is employed. Equation (5) represents the microturbine operating cost. Equation (6) represents the magnitude of the power limit in each section. Further, equation (7) represents the maximum power in the first segment. Equations (8) and (9) represent technical features that represent the rising and falling speeds of the generated energy as rising and falling constraints. Equation (10) calculates the minimum rise and fall times used from the two Up and Dn time auxiliary variables shown in (11);
equation (16) — (21) represents the energy storage system limit. Equation (16) represents the energy stored in the battery before calculation. Equations (17) and (18) represent the highest possible charge and discharge energy of the battery. Equation (19) represents the reserved energy range. Equation (20) prevents the storage system from being charged and discharged at the same time. Finally, equation (21) calculates the reserve energy in the battery at any given time.
Step 4, modeling wind power and solar energy:
equation (12) calculates the wind production over a given time, and equation (13) limits the energy used by the wind turbine production.
Pt wind≤Pt wind,max(19)
And (3) modeling solar power generation, namely utilizing solar panels to generate electric energy by utilizing solar energy. Furthermore, the maximum energy produced from this type is generally assumed. Equation (14) calculates the solar power generation over a given time, calculating the maximum energy production. Equation (13) limits the energy used by the solar panel.
Step 5 modeling the demand response plan:
the TOU change of the DeRP is realized in the step. DeRP smoothes load distribution and reduces operating costs by moving energy consumption from peak times to other times. With DeRP, a portion of the consumption can be moved from peak hours to other times. The consumption pattern generated using DeRP can be calculated by equation (22) and it is reformed into equation (23).
Further, the technical limit of DeRP is expressed in equation (24) — (27).
According to equation (24), the total consumption does not change over a period of time; it will only shift from peak hours to other hours. In equation (25), it is also shown that the increased load must be less than a percentage of the base load. According to (26) and (27), the contribution amount to the DeRP should not exceed a predetermined value. In step, 20% is set to a predetermined value.
Step 6 provides a solving algorithm and an evaluation method:
an evaluation method is provided by using a pareto filter algorithm, and firstly, a measurement space model of a double-target case is evaluated. Second, a set of evenly spaced points on the optimal line for the dual target problem is determined. And thirdly, applying a conventional constraint method to the double-target problem for the optimal hyperplane of the three-objective function, and finally obtaining evenly spaced points on the optimal plane, wherein the specific scheme is shown in the attached drawing as a final evaluation result.
Claims (1)
1. A hybrid power generation system and demand response based industrial user energy usage assessment method is characterized by comprising the following steps: the method comprises the following steps:
step 1, modeling the energy classification provided for industrial customers in different modes;
step 2, determining power balance constraint and bilateral contract limit;
step 3, evaluating the energy production of the internal system and the energy storage system;
step 4, evaluating the generated energy of the wind power and solar energy system;
step 5, evaluating the energy consumption for participating in demand response;
and 6, providing a solving algorithm and an optimizing method.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985157A (en) * | 2020-08-19 | 2020-11-24 | 西华大学 | Power utilization model simulation method based on industrial production process |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985157A (en) * | 2020-08-19 | 2020-11-24 | 西华大学 | Power utilization model simulation method based on industrial production process |
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Application publication date: 20200117 |