The VPP operator integrates the electric bus fleet within the operational planning of its power plant portfolio as detailed in [
21]. Let
be the set of unit types used in the power plant portfolio, consisting of the wind power plant (wind), photovoltaic power plant (pv), combined heat and power plant (chp), electric vehicles (ev), and industrial load units (ind). A multi-period optimization process is applied within the the energy management. This allows considering distinct forecast horizons
to determine mid-term and short-term bidding schedules. The classification of the forecast horizons is derived from the trading period and clearing sequence of joint market operations in day-ahead and intraday markets. The breakdown of generation and load schedules yields a more efficient use of the energy sources in market tradings [
22]. Further, the VPP operator integrates shorter dispatch intervals to eliminate market framework barriers for the participation of renewable energy sources.
Using the information provided by the EVS/A and taking the depot characteristic discussed in
Section 3,
Table 2 gives a possible sample set used for the evaluation purposes of the proposed methodology. In this example, a total number of 193 buses is integrated into the power plant portfolio. The rated energy capacity
of the bus batteries is 41.53 MWh. The forecasted daily energy demand
for the first-base charging assessments is 10.59 MWh.
With regards to the integrated electric bus fleet, the VPP operator determines optimized charging schedules for each stage of the multi-period optimization process. This VPP service is provided to the EVS/A, which buys the electricity and responds to requests for the adjustment of charging schedules. First, the economic efficiency and feasibility of processing the optimized charging schedules are investigated. Then, the potentials for offering optimized redispatch measures are assessed as part of the extreme condition tests.
4.1. Implementation Model and Mathematical Formulation
The introduced unit models given in
Table 1 are transferred into boundary and constraint conditions in the optimization model of the VPP operator. The boundary conditions are reflected by means of the provided
and
matrices of the EVS/A. The optimization problem combines the optimization variables given by the power dispatch
of all energy sources in the power plant portfolio, as well as the contracted market biddings
.
Therefore, the VPP operator applies (
9), giving the cost-optimizing bidding strategy aiming to minimize the variable cost, while maximizing the relative gross profit. Hereby,
indicates the variable operating cost for each unit type, while
defines the energy market price in day-ahead and intraday markets.
The bidding strategy is subjected to the operating ranges
and
for the overall power generation and demand. In case of storage units including the electric bus fleet, the constraint formulation is given by:
The binary variables
specify the operation mode of the distinct units. The available energy capacity values are derived from (
11) as a function of the assigned rated energy capacity with regards to the vehicle models given in
Table 1.
The dispatched power is bounded by the state of energy limits
, with
. In each stage of the multi-period optimization process, the power balance
of the power plant portfolio is calculated by applying (
12) and (
13).
The terms refer to the total power generation and total power demand of the power plant portfolio for each time step k. Market imports and market exports are indicated by and , respectively.
4.2. Computational Study and Dispatch Results
The mixed-integer linear programming problem is solved by using a branch-and-cut method with simplex algorithm, offered by the MATLAB extension of the ILOG CPLEX optimization solver. In each stage, the bidding schedules are optimized while considering the unit type specific boundary and constraint conditions, including updated information and operational states formulated in
Section 4.1. With a focus on the electric vehicle fleet,
Figure 9 provides the obtained charging schedules for the first-base charging at depot. During day-ahead and intraday operation, the charging schedules are determined based on the forecasted power generation and demand of the installed renewable generation and load units within the power plant portfolio.
For validation purposes, the average energy capacity of the electric bus fleet after the charging processes , normalized on the rated energy capacity , is given by the light gray area. As can be seen, the proposed methodology ensures the operability of the electric bus fleet by keeping the average energy capacity between 80% and 100%. The available energy capacity at the depot , normalized on the rated energy capacity , is given by the dark gray area. At local peaks of the available energy capacity, specifically at 2:00 and 2:30, possible options for vehicle to grid operations are determined. However, during intraday operation, these services are not explicitly utilized due to updated information, e.g., requests for power system services. Besides, the charging schedule determined during intraday operation follows the day-ahead charging schedule, taking into account more precise forecasts of power generation and demand.
Addressing even more enhanced energy management and supply solutions by providing system services and redispatch measures, several positive
and negative
control reserve requests of the system operator are investigated in the extreme condition test. The VPP operator reacts with optimized redispatch measures and calculates an alternative charging schedule.
Figure 10 shows the charging schedules with and without considering the positive and negative control reserve requests. Here, the provision of 0.5 MWh (case 1), 1 MWh (case 2), and 2 MWh (case 3) through redispatch measures and hence adjusting the charging power is evaluated.
The results show that every positive control reserve request can be fulfilled through charging power adjustments and vehicle to grid services. While this also applies to the first scenarios of negative control reserve requests, the peak request of 2 MWh cannot be fulfilled due to insufficient available negative reserve capacity. The infeasible solution is highlighted in
Figure 10f. In this extreme condition test, the request of the system operator is denied by the VPP operator. In summary, the feasible solutions for the provision of system services and redispatch measures for an entire day are detailed by means of
Figure 11, which shows the available reserve capacities at the depot. Giving insight into the simulation results obtained by testing the 2-MWh negative control reserve request,
Figure 11d provides further details. The infeasible solution is caused due to the reduction of available negative reserve capacity, which is completely reduced to zero. The available positive control reserve requests remain the same for every case. This effect is due to the intended charging strategy that keeps the electric bus fleet at a high state of energy ranges, ensuring a high readiness for use of the electric bus fleet.
The simulation results of the performed extreme condition tests prove the possibility for the provision of additional system services. This is achieved by optimally adjusting the charging schedules, while considering the boundary and constraint conditions, including updated information and the operational state for the operation of the electric bus fleet. Overall, the additional constraints given by the temporal availability and energy demand profiles of electric bus fleets are fully reflected in the optimization model. This allows achieving optimal charging solutions while fulfilling the contract position with the EVS/A. Further, the power provided by renewable energy sources can be optimally utilized for charging processes.