Abstract
This study focuses on the application of the Bellman algorithm, one of the algorithms of dynamic programming for energy management in a microgrid with five distributed energy sources.
The management strategy of the microgrid is based on long-term planning. The latter requires that each of the distributed sources follow an optimal active power profile established the day before for the next day (D-1). Thus, the optimization strategy proposed in this study belongs to the deterministic optimization strategies, whose objective is to find the optimal control of power flows in the multi-sources micro grid. The optimal control means how to distribute the power flows among the different components of the microgrid in order to ensure the balance between production to demand with the lowest cost. The performance of dynamic programming is relative to the refinement of the discretization of the planning trajectory and reference signals. Therefore, the quality of the optimum found is sufficiently guaranteed at the expense of a consequent demand on computing and memory capacity.
The aim of this study is to establish a management by the application of the Bellman algorithm (dynamic programming) and to compare it with the strategy of the rules-based management to open later on other meta-heuristic techniques such as the genetic algorithm. The obtained results showed that the implementation of the proposed energy management strategy resulted in a 40% saving on the electricity bill.
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Abbreviations
- T:
-
Planning trajectory (24h).
- t:
-
The Hourly Step (l h).
- Cgrid:
-
The electricity purchase tariff (consumption bands) in €/Kwh.
- Creseau:
-
The cost of electricity in €/hour.
- Pinjct:
-
The power injected into the network in KW.
- Pbatmax:
-
Maximum power that can be exchanged by batteries in charge/discharge in W.
- Cgridi:
-
The injection rate (injection tranches) in €/Kwh.
- SOC:
-
State of charge of batteries in %.
- SOH:
-
Battery health status in %.
- ΔSOC:
-
Change in battery state of charge in %.
- SOCmin, SOCmax:
-
Minimum and maximum stop of state of load in %.
- SOHmin:
-
Minimum battery health limit in %.
- ΔSOCmax:
-
Maximum limit of the variation of the state of charge in %.
- Cess:
-
Operating cost of batteries (cost of wear) in €/hour.
- Cinv:
-
Investment cost of batteries in €/kWh.
- \({\varvec{\upeta}}\) :
-
Efficiency of the micro-gas turbine in %.
- Ptgn:
-
Rated active power of the micro-gas turbine in kW.
- Ptg:
-
Active power exchanged by the micro-gas turbine in kW.
- Mg:
-
Mass of natural gas consumed by the micro-gas turbine in kg.
- MNOx, MCO, MC02:
-
Mass of gases emitted by the micro-turbine: NOX, CO and C02 in g.
- MC02eq:
-
Mass of C02 emissions equivalent in kg.
- Cexptg:
-
Cost of production of active power by the micro-gas turbine (Fuel cost) in €/hour.
- CC02eq:
-
Environmental sustainability due to C02 emissions equivalent in €/hour.
- CON/OFF:
-
Penalty for stopping/running the micro-gas turbine in €/hour.
- Pgrid:
-
Main network power in W.
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Atifi, Y., Raihani , A., Kissaoui, M. (2022). Smart Grid Production Cost Optimization by Bellman Algorithm. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham. https://doi.org/10.1007/978-3-031-20490-6_18
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