PROCEEDINGS OF ECOS 2014 - THE 27TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS JUNE 15-19, 2014, TURKU, FINLAND
A Multi-Agent Based Approach for Energy Management in Microgrids Hassan Harba, Thomas Schütza, Rita Streblowa and Dirk Müllera a
Institute for Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, E.ON Energy Research Center, Aachen, Germany,
[email protected]
Abstract: A shift to a decentralized power generation mainly based on Renewable Energy Sources (RESs) is essential to meet the climate protection goals of reducing greenhouse gas emissions. The fluctuating generation of RESs poses challenges for the stability of the electrical grid. Therefore, energy management systems (EMSs) are essential to cope with the volatility of RESs through an intelligent coordination of Distributed Energy Resources (DERs). Thus, EMSs ensure the security of energy supply and enhance the integration of RES. In this work, an energy management strategy based on a multi-agent system (MAS) for a microgrid is presented. The control strategy exploits the flexible operation of the DERs deployed in heating systems, which arises from using thermal storages, to adapt to the RESs fluctuations. This is performed by determining optimized schedules for the DERs using Mixed Integer Linear Programing (MILP) as well as thermal and electrical demand forecasting models. The coordination of the DER within the microgrid is achieved through a centralized and a decentralized approach for determining the operation schedules. Both approaches are assessed against a scenario in which the conventional heat driven operation of DERs is employed. The evaluation criteria comprise Primary Energy Consumption (PEC), reduction of CO2 emissions, operation costs and integration of RES. Both centralized and decentralized approaches achieve significant reduction in PEC and CO2 emissions compared to the heat driven scenario. The centralized approach achieves the highest coordination level by minimizing the import of external electricity and the highest integration of RESs. The decentralized approach is characterized by a high electricity export and achieves the lowest PEC and lowest net CO2 balance.
Keywords: Multi-agent system, Demand side management, Scheduling, Renewable energies, Distributed energy resources
1. Introduction The German government set itself ambitious targets for energy and climate policy. By 2050, greenhouse gas emissions are to be cut by 80% compared to the 1990 level [1]. Further, 80% of the electricity production is to derive from Renewable Energy Sources (RESs) [1]. The transition of the energy system to a decentralized power generation is internationally known as the “Energiewende” or energy reform. However, the generation of renewables, mainly photovoltaics and wind energy is weather dependent and consequently volatile. Hence, the energy reform with renewables as a cornerstone, poses several challenges for the stability of the electrical grid and the security of the supply. Therefore, energy management systems are required to cope with the volatile generation of RESs, through coordination of Distributed Energy Resources (DERs), i.e. Combined Heat and Power (CHP) and Heat Pump (HP) units. In this work, a distributed energy management strategy based on a multi-agent system (MAS) for microgrids is presented. The MAS is employed as a framework for the control and communication to enable the reliability and scalability of the energy management solution. The MAS concepts are implemented in JADE [2] (Java Agent DEvelopment framework), which is an open-source platform that complies with the current agent standards, such as FIPA (Foundation for Intelligent Physical 1
Agents) specifications [3]. Thermal storage systems allow for flexible operation of DERs employed in heating systems. The control strategy exploits this flexible operation of the DERs, to adapt to the RES fluctuations. This is performed by determining optimized schedules for the DERs using Mixed Integer Linear Programing (MILP) as well as thermal and electrical demand forecast models. The scheduling is determined for the next 24 hours in 15 minutes timestamp. The schedules are then vectors consisting of 96 elements. The MILP system is modeled with GAMS [4] and solved with CPLEX [5].
2. Coordination A microgrid is an electrical system comprising multiple interconnected loads and DERs within clearly defined boundaries. This system acts as a single controllable entity with respect to the broader electrical grid “macrogrid“ and can operate either connected to the macrogrid or disconnected in an island mode by meeting internally the local demand [6]. Hence, microgrids are important elements of future smart grids. The internal loads and DERs can be controlled through several approaches, according to the energy economy and policy boundaries. In this work, we investigate a MAS based strategy with a centralized and a decentralized coordination approach. The centralized coordination scheme simulates a Demand Side Management (DSM) concept [7] in which the DERs are scheduled collectively to achieve a common goal, while meeting the individual electrical and thermal demand. In the decentralized approach, the schedules of the DERs are not directly determined but induced through dynamic and variable electricity pricing. Hence, the latter approach is defined as a Demand Response (DR) strategy [7]. In this work, the investigation of the performance of the scheduling strategies is restricted to a grid connected scenario for the microgrid.
2.1. Demand side management The MAS for DSM is depicted in Fig.1 and comprises five agents with specific functionalities: aggregator, house, renewable, trader, and weather. The weather agent performs or queries a forecast of the outdoor air temperature, solar irradiation, and wind speed and forwards this information to the house and renewable agents. A house agent uses the outdoor temperature prognosis as well as recorded heat demand and outdoor temperature of the past periods to forecast the heat demand for the next day and forwards this result to the aggregator agent. The thermal demand forecast algorithm is based on a time series approach. The heat demand is broken down into a systematic and a behavioral component. The systematic component is determined by building a heating curve which correlates the heat demand to the outdoor temperature. The behavioral component is derived from computing a forecast of the heat demand previously measured. The house agents send their device characteristics (modulation level, nominal power and efficiency) to the aggregator agent. Along with the heat demand forecasts, these characteristics are used as input parameters and restrictions for the schedules’ optimization. The renewable agent uses the wind speed and solar irradiation forecasts to determine the available solar and wind energy and forwards this information to the aggregator. In the DSM scheme, the aggregator acts as the main coordinator of the DERs. The functions of the aggregator comprise electrical demand forecast (of appliances and lights exclusively), schedule optimization and the aggregation of total electrical balance. The schedule determination is based on a MIL program which is introduced in the next section. The electrical demand forecast is carried out on a microgrid level for all the houses collectively, thus reducing the forecast error. The prediction algorithm is an adjusted version of the additive Holt-Winters model with daily and weekly seasonality [8]. The smoothing parameters which influence the accuracy of the forecast are updated to the optimal values that are determined by using a metaheuristic optimization algorithm, Simulated Annealing [9]. The aggregated electrical balance in the grid connected model includes the electrical lack and surplus and is forwarded to the trader agent. The latter ensures that the surplus is exported and the lack is compensated from other micogrids or the macrogrid. In island 2
mode, the trader has no role as the loads are matched by the internal DER solely. This is realized by adjusting the restrictions for the schedules’ optimization. Further, a battery is introduced in the island mode to ensure the convergence of the optimization as a variable sink and source of electricity.
Fig. 1. MAS structure for DSM.
2.2. Demand response In the MAS structure for DR depicted in Fig. 2, one additional market agent is included in the above presented agents’ system. Several agents maintain the same functions mainly the weather, renewable and trader agents. The aggregator agent is no more the main coordinator and its functions are reduced to aggregating the total electrical demand as well as lack and surplus. The functions of the house agents increase to include the individual electrical demand forecast and the schedule generation. The steering signal for the scheduling on house level is provided by the market agent as a dynamic price signal. The aggregator agent receives the electrical demand forecast and schedules of the DERs from the houses and offsets these against the available renewables. The residual demand as well as the available renewable energy and the device characteristics are forwarded to the market agent, which computes the price signal. The day-ahead schedules are determined on house level based on an iterative approach. The price signal is initiated by the market by comparing the sum of all electrical demand forecasts with the available renewables. The iteration begins by applying this price signal to the largest CHP. In the following steps, the price signal is updated according to a merit order scheme resulting from the onoff-state of each DER and the interaction with the macrogrid. These updated price signals are used to schedule HPs and CHPs alternatingly. If all restrictions are obeyed, each house has scheduled the corresponding heating devices at least once, and the price signal following two consecutive iterations remains unchanged, the process has reached convergence. The alternating scheduling of CHPs and HPs has proved to converge with the minimum amount of iterations.
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Fig. 2. MAS structure for DR.
3. Mixed integer linear optimization model This section describes the mathematical models applied in the centralized, decentralized and heat driven scheme. First, the models for the common devices, the thermal storage unit and the boiler are explained. Next, the models for the HP and CHP are presented, together with the objective functions used in the corresponding, decentralized scheme. Subsequently, additional equations for modeling the interaction in the centralized as well as the heat driven scheme are formulated. For all models we assume time intervals of fifteen minutes, resulting in .
3.1. Thermal storage unit The average temperature of the thermal storage unit can be derived from balancing the time rate of change of the inner energy of the storage unit as a result of the generated heat, the heat demand of the building, and the storage’s losses. In order to account for transmission losses between the heat generator, the storage unit, and the building, efficiency values for the charge and discharge of the storage unit are introduced. ̇
̇
(
̇
)
(1)
The storage’s losses are assumed to result from heat conduction over the storage’s surface, when neglecting the temperature gradient inside the storage and considering a constant temperature of the storage’s surroundings. Consequently, ̇ is calculated according to Fourier’s law of heat conduction: ̇
(
)
(2)
The storage’s temperature is bounded by .
and initialized with
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3.2. Boiler The boiler is modeled to be able to run in a continuous band between a threshold ̇ nominal load ̇ . ̇
̇
̇
and the (3)
3.3. HP units The HP is assumed to operate between two discrete (on/off) modes. The generated heat from the HP unit and the boiler can be computed as the sum of both devices. The nominal output as well as the COP are determined in function of the outdoor temperature. ̇
̇
̇
(4)
The HP’s electrical consumption results in ̇
.
(5)
In the decentralized scheduling for the heat pump equipped houses, the objective function is the minimization of the boiler’s gas costs as well as the electrical costs for the HP unit. ∑
̇
[
]
(6)
3.4. CHP units The CHP similarly to the HP operates in on/off modes. The total generated heat from the CHP unit and the boiler results in: ̇
̇
̇
(7)
The electricity generation of the CHP unit is computed to ̇
.
(8)
The overall electricity balance is .
(9)
In order to prevent simultaneously buying electricity from the grid and providing electrical surplus, (10) and (11) are introduced. In (11), an upper bound for the electrical surplus is given with ̇ . (10) (
)
(11)
The objective of the decentralized scheduling of the CHP equipped houses is the maximization of the total profit, resulting from the revenue from providing electrical surplus, less the costs for additionally required electricity and the gas costs for boiler and CHP usage. ∑
[
(
̇
̇
)]
(12)
3.5. Centralized scheduling In the centralized scheduling, (1)-(5) and (7)-(8) are used for each house. The overall electrical balance is reformulated to account for the electricity consumption of the HPs as well as the electricity generation of renewables. 5
∑
∑
(13)
Additionally required electricity and electrical surplus cannot be both unequal to zero at the same time step, thus the following two equations are used. The upper boundary in (14) is ̇
∑
. ∑
In (15), the upper boundary is set to
̇
. (14)
(
)
(15)
The objective in the centralized scheduling is similar to (12), taking into account the boilers installed in the HP equipped houses. ∑ ̇
With ̇
̇
(
)
(16)
being defined as: ∑
(
̇ ̇
̇
∑
)
(17)
3.6. Heat driven scheduling In the heat driven scheduling, all equations applied in the centralized scheduling can be reused. Furthermore, the device’s order has to be taken into account. (18) (19) The heat driven scheduling yields at minimizing the generated amount of heat: ∑
[(∑
̇
)
(∑
̇
)]
(20)
4. Simulation and results This preliminary proof of concept investigation considers a microgrid comprising four houses; two are equipped with CHP units and boilers while the others use HP units together with boilers. The DERs are designed as bivalent systems. Typically, CHP units are selected based on the load duration curve to reach a minimum of 4000 full load hours per year, to ensure reasonable amortization times [10]. In this scenario, we seek maximum integration of renewables, thus the CHP capacity is selected to operate 2000 full load hours per year to provide high electrical reserves and compensate the fluctuating RES and to enable the island operating mode. Consequently, the CHP units are oversized by ~ 60% compared to the standard design’ capacities. The HP units are dimensioned according to typical design rules [11] and have a bivalence point at -5 °C. Table 1 lists the maximal thermal demand that occurs within the simulated period, the nominal thermal outputs of each heating device as well as the size of the installed thermal storage. The RES included in the microgrid are wind and photovoltaics, each with a peak output of around 12 kW.
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Table 1. Heating devices dimensioning. Max. demand Main device Peak load boiler (kWth) (kWth) (kWth) CHP 1 CHP 2 HP 1 HP 2
27.4 22.5 6.1 15.4
19.2 16.0 7.0 15.0
10.8 9.0 5.0 14.0
Storage (L) 1000 1000 200 500
The prices and remuneration applied in the investigated scenario are listed in Table 2. The gas and electricity prices are extrapolated from historical data, provided by the German Federal Statistical Office [12]. The feed-in remuneration is set to the expected average electricity production costs of renewables in the year 2020 [13] and is therefore higher than today’s remuneration. This parameter is congruent with the objective of integrating as much RES as possible. Table 2. Energy prices and remuneration. Costs of natural gas (ct/kWh) Costs of electricity (ct/kWh) Remuneration for feed-in (ct/kWh)
8.50 35.00 11.00
Exemplary results of the centralized scheduling are shown in Fig. 3. The first subplot shows that the interaction with the grid is held to a minimal level. Between hours 8 and 12, when renewables outweigh the electrical demand, the scheduling does not produce large surplus, but instead runs the HP units with this available energy. During the peak demand between hours 15 and 21, the scheduling minimizes the additionally required amount of electricity by activating the CHP units. The HP and CHP scheduling is strongly coordinated with the grid interaction. During peak electrical demands, the CHP units run and HP units are deactivated. On the other hand, the CHP units are turned off, when electrical surplus is available. At such times either the surplus is sold to the grid, or HP units are started to exploit available electricity for meeting the thermal demand or charging the storage unit. The remaining time steps during which the HP units run are synchronized with the CHP units. As a result, the additionally purchased electricity is reduced, like done between hours 2 and 7.
Fig. 3. Exemplary results of the centralized scheduling for one day. 7
For quantitatively assessing the coordination of each scheduling scheme and thus the cooperation of CHP and HP units, a new index is introduced in (21). This degree of coordination is defined as: ∑
[∑
(
)
].
(21)
A low value for indicates a highly coordinated scheduling, which is achieved by turning on the HP units when little amounts of additional electricity are required or while the microgrid provides electrical surplus. The values for in each scheme are listed in Table 3. Table 3. Degree of coordination for the centralized, heat driven, and decentralized scheduling. (
)
Centralized 0.32
Heat driven 50.33
Decentralized 29.76
The interaction with the grid is illustrated in Fig. 4. The solid, black curve depicts the annual average, electrical demand of the microgrid for appliances and lighting. The other lines stand for the annual average, electrical surplus, or the additionally required electricity, if the value is below zero. The shaded areas represent the range between the minimum and maximum values of the centralized and decentralized schemes that occur at the corresponding time step. The range of the heat driven scheme is not plotted, because it is not significant and decreases the readability. Overall, the heat driven strategy performs worst, because it has the highest dependency on the grid, and provides the lowest amount of electrical surplus. It requires the largest support from the grid during peak demand, thus destabilizing the grid. The centralized strategy on the other hand performs best. The shaded ranges especially show the strength of the centralized scheduling. While the maximum values of these ranges are similar for the centralized and decentralized strategies, the minimum values show that the centralized strategy requires less additional electricity, even being able to reduce the peak demand that occurs between hours 18 and 22 to almost zero. The average curve of the centralized scheduling is very well balanced, this way the centralized control is capable of functioning as a virtual power plant that could reduce the loads regular power plants face and balance the demands of the grid. Despite the fact that the decentralized control generates similar amounts of electrical surplus as the microgrid’s participants consume, the decentralized strategy is less suitable for supporting the grid, because the surplus is very volatile, as the shown ranges indicate.
Fig. 4. Grid interaction for centralized, heat driven, and decentralized scheduling. Table 4 lists the annual operating costs for all three investigated control strategies. The heat driven strategy serves as a reference for the centralized and decentralized scheduling. The costs’ flow is 8
decomposed into the costs for gas usage, electricity bought from the macrogrid and the revenue generated from feed-in. The overall costs balance is the sum of the electricity and gas costs, less the revenue generated from providing electrical surplus. As shown in Table 4, the overall costs balance is worst for the heat driven strategy and best for the centralized scheduling. The heat driven scheduling reduces the gas costs to a minimum level, but by completely neglecting the electrical side, the overall costs for this strategy are by far the highest. The centralized scheduling is able to reduce its dependency on the grid at the costs of increased gas consumption and less revenue from electrical surplus, compared with the decentralized scheduling. The centralized strategy reaches the lowest operating costs, because the costs of additional electricity outweigh the gas costs, as shown in Table 2. Moreover, it is reasonable to shift loads in order to prevent the purchase of additional electricity by decreasing the electrical surplus, because the remuneration for surplus is lower than the costs for additional electricity. The decentralized approach is less well coordinated, thus resulting in higher revenues from selling surplus, while simultaneously purchasing more additional electricity. Table 4. Annual operating costs. Heat driven 21965.79 7040.36 -4090.56 29006.15 24915.59
Costs for gas (€) Costs for electricity (€) Revenue from feed-in (€) Sum of gas and electricity costs (€) Overall costs balance (€)
Centralized Decentralized 16.97% 7.92% -90.56% -38.74% 27.62% 35.97% -9.13% -3.41% -15.16% -9.87%
The annual primary energy consumption is listed in Table 5. For computing the gas consumption, boilers are assumed to have an efficiency of about 90% and CHP units 35%. The PEC for electricity and the avoidance of PEC from feeding-in electrical surplus are computed by using an average efficiency of external power plants of 42% [14]. The overall PEC balance results from subtracting the avoidance from feed-in from the sum of the gas and electricity PEC. The decentralized strategy achieves the lowest overall PEC balance, because it generates the highest amount of electrical surplus, and as a consequence leads to the highest avoidance of external power plant usage. The heat driven strategy has the highest overall primary energy consumption, although it requires the least amount of gas, but on the other hand strongly depends on additional electricity from the macrogrid. Table 5. Annual primary energy consumption. Heat driven Centralized Decentralized 257330.39 17.47% 8.37% 46563.25 -90.29% -36.99% -88540.26 27.62% 35.97% 303893.64 0.96% 1.42% 215353.38 -10.01% -12.78%
PEC for gas (kWh) PEC for electricity (kWh) Avoidance of PEC from feed-in (kWh) Sum of PEC for gas and electricity (kWh) Overall PEC balance (kWh)
The annual CO2 emissions are coupled with the primary energy consumption and are shown in Table 6. According to [15,16], the CO2 emissions for 1 kWh gas are 202 g and 601 g for 1 kWh electricity. The results are similar to the aforementioned PEC, indicating that the decentralized scheduling performs best concerning the ecological factors, because it provides the highest amount of electrical surplus, thus avoiding the utilization of regular power plants.
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Table 6. Annual CO2 emissions. Heat driven Centralized Decentralized CO2 emissions of gas (t CO2) 51.98 17.47% 8.37% CO2 emissions of electricity (t CO2) 11.75 -90.29% -36.99% Avoidance of CO2 emissions from feed-in (t CO2) -22.35 27.62% 35.97% Sum of CO2 emissions of gas and electricity (t CO2) 63.73 -2.41% 0.01% Overall CO2 emissions balance (t CO2) 41.38 -18.62% -19.42%
The sensitivity of increasing the participants’ number is analyzed through preliminary investigations of the algorithms’ performance with 34 houses. In the centralized scheme, the MILP computing time for the scheduling increased by a factor of 60, from ~3 seconds for 4 houses to ~180 seconds for 34 houses. This shows that the centralized coordination time within a single microgrid increases exponentially with the number of agents and can only be applied as expected, to a limited number of participants. The scalability of such a solution can be achieved through intermicrogrid coordination between the aggregators of different microgrids. In the decentralized scheme, the increased number of participants has no effect on generating the optimized schedule which is carried out by the house agents in less than 1 second. However, the convergence of the price consistency which is pursued by the market agent using an iterative process is achieved in the 4 houses setup within 10 iterations, while in the 34 houses setup the convergence is reached within 90 iteration steps. This can pose a main drawback for further increasing the number of participating agents in the decentralized concept.
5. Conclusion In this work, two concepts are introduced for enhancing the integration of renewables into the power system and ensuring supply security. In the framework for both concepts, clusters of distributed loads and generation sources are grouped to build microgrids. This results in a distributed system that ensures the scalability and robustness of the proposed energy management solution. Both concepts are multi-agent based approaches for microgrids, in which DERs are coordinated to cope with the fluctuating renewable supply. In the DSM approach, a collective common interest is the main target. The schedules for the participating house agents are carried out by a single coordinator within a microgrid. In the DR approach, the individual interest is dominant and the scheduling is determined by the house agents themselves within the microgrid using an iterative approach in which the electricity price is adjusted to achieve the optimal schedule. The developed strategies are evaluated against the conventional Heat Driven (HD) control strategies for DERs. The centralized approach achieves the highest coordination degree. HPs are operated during periods of RESs availability while CHPs are operated to avoid the import of electricity. Consequently, DSM accomplishes 15.16% and 6.23% reduction in the overall costs balance compared to HD and DR respectively. The decentralized scheduling leads to a longer operation of CHP units and to larger exports of surplus electricity. As a result, the balance of primary energy consumption for DR is 12.78% and 3.09% lower than for HD and DSM respectively. Further, DR and DSM achieve an avoidance of 19.42% and 18.62% in the CO2 emissions compared to the HD.
Acknowledgement Grateful acknowledgement is made for financial support by E.ON gGmbH.
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Nomenclature Letter symbols A Area, m2 COP Coefficient of performance of a HP unit, M Upper boundary for interaction with the macrogrid, W P Electrical power, W ̇ Heat flux, W T Temperature, K U Insulation coefficient, W/(m2 K) V Volume, m3 b Binary variable indicating if the boiler is activated, c Specific costs, € d Binary variable indicating if additional electricity is purchased from the macrogrid, k Heat capacity, J/(kg K) p Remuneration for electrical surplus, €/W t Time, s v Binary variable indicating if the CHP or HP unit is activated, Greek symbols Efficiency, Density, kg/m3 Electrical efficiency of CHP units, Subscripts and superscripts add Additionally required boil Boiler ch Charge dch Discharge dem Demand el Electrical g Index representing the CHP units gen Heat generation h Index representing the HP units init Initialization ins Insulation loss Losses ren Renewables rev Revenue srp Surplus sto Storage sur Surroundings
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