Florian Salah

Deadline differentiated pricing in practice: marketing EV charging in car parks

Electric vehicle charging is considered as a prime case of load flexibility in future smart grids. We examine a scenario where electric vehicles are charged in a car park with local photovoltaic (PV) generation. In this setting, temporal charging flexibility can be leveraged to increase utilization of local generation. To incentivize flexible loads we apply a deadline differentiated pricing scheme. Prices are set by the car park operator in a profit-maximizing manner in settings with varying PV capacity and costs of conventional generation. This allows us to assess the value of flexibility passed on to customers in form of discounts. Furthermore, we determine the minimum flexibility level qualifying for this discount. By and large, absolute price levels and flexibility discounts are mainly driven by the cost of conventional generation. On the other hand, the minimum flexibility requirements are affected by both the costs of conventional generation as well as the local PV capacity: higher costs of conventional generation as well as larger PV capacities will decrease this threshold.

Rate-Constrained Energy Services in Electricity

The integration of renewable generation poses operational and economic challenges for the grid. For the problem of power balance, the legacy paradigm of tailoring supply to follow demand may be inappropriate under deep penetration of renewable generation. In this situation, the alternative approach of controlling demand to follow supply offers compelling benefits in terms of reduced regulation costs. This paper considers rate-constrained energy services (RCESs) which are a specific paradigm for flexible demand. These services are characterized by a delivery window, the total amount of energy that must be supplied, and the maximum rate at which this energy may be delivered. We consider a forward market where RCES are traded. We explore allocation policies and market decisions of a supplier in this market. The supplier owns a generation mix that includes some uncertain renewable generation and may also purchase energy in day-ahead and real-time markets to meet customer demand. The supplier must optimally select the portfolio of RCES to sell, the amount of day-ahead energy to buy, and the policies for making real-time energy purchases and allocations to customers to maximize its expected profit. We offer solutions to the supplier’s decision and control problems to economically provide RCES. We provide numerical results illustrating our finds.

Pricing of demand flexibility: Exploring the impact of Electric Vehicle customer diversity

Electric Vehicles (EVs) are a large, but also quite flexible load in the power system. Car parks constitute major future load clusters that need to coordinate charging requests from EVs according to local grid and supply conditions. For effective grid integration, it is necessary to understand how to influence the charging behavior of EV customers. A deadline differentiated pricing approach is employed to create incentives for EV customers to offer their load flexibility to the car park operator. We explore the effect of different utility diversity models and flexibility levels of EV customers in a car park scenario under consideration of local photovoltaic power generation. Our results indicate that a homogeneous customer utility model overestimates the car park operator profits by more than 17% as compared to a realistic heterogeneous model. Furthermore, we observe that the car park type, and thus the customer parking time also drives the attained profits.

Portfolio Design of a Demand Response Aggregator with Satisficing Consumers

This paper studies the impact of consumer behavior on the portfolio design of a demand response (DR) aggregator. Consumer behavior is modeled using elements of satisficing theory. We develop an optimization model to decide the optimal portfolio of DR contracts for an aggregator participating in the electricity market. In our model, the aggregator must pay a premium to enable the participation of consumers who have a certain aspiration threshold, below which they will not participate. Thus, the proposed model determines the premiums to be offered to consumers in order to obtain a DR portfolio that maximizes the aggregator’s operating surplus while satisfying the aspirations of participating consumers. Several simulations are performed to obtain insights on the value of the DR resource, and the importance of parameters used to model the consumer behavior.

Efficiently solving DSM problems: Are we there yet?

With an increasing amount of renewable energy generation, the scheme of supply following demand is no longer viable. As a consequence, aggregating entities (e.g., utilities, service providers) have to find new ways to balance demand and supply in order to guarantee an economic and environmental friendly operation of the energy grid. An approach recently extensively studied is the concept of duration-deadline jointly differentiated energy services that elicits temporal flexibility of the demand side. This paper considers different mathematical models that can be used to solve this demand side management problem applied to an electric vehicle charging use case. A classically applied approach (referred to as classic approach) uses a three-dimensional allocation matrix whereas a specially designed approach for this problem class (referred to as multiple deadline approach) uses majorization theory to answer the questions of adequacy and adequacy gap. These approaches are compared in regard to their time to create and time to solve the optimization problem as well as their sensitivity towards an increasing number of customer, deadline, and scenarios of renewable power generation. The results show that computation time of the classic approach is strongly influenced by the number of scenarios and customers whereas computation time of the multiple deadline approach is strongly influenced by the number of deadlines and scenarios. Neither of the approaches can be described as superior to the other as both react differently to input data. Furthermore, the results show that for a large-scale implementation both approaches must be improved in their complexity to ensure a continuous operation.

Efficiently solving DSM problems : Are we there yet? : A Real World EV Use Case [in press]

With an increasing amount of renewable energy generation, the scheme of supply following demand is no longer viable. As a consequence, aggregating entities (e.g., utilities, service providers) have to find new ways to balance demand and supply in order to guarantee an economic and environmental friendly operation of the energy grid. An approach recently extensively studied is the concept of duration-deadline jointly differentiated energy services that elicits temporal flexibility of the demand side. This paper considers different mathematical models that can be used to solve this demand side management problem applied to an electric vehicle charging use case. A classically applied approach (referred to as classic approach) uses a three-dimensional allocation matrix whereas a specially designed approach for this problem class (referred to as multiple deadline approach) uses majorization theory to answer the questions of adequacy and adequacy gap. These approaches are compared in regard to their time to create and time to solve the optimization problem as well as their sensitivity towards an increasing number of customer, deadline, and scenarios of renewable power generation. The results show that computation time of the classic approach is strongly influenced by the number of scenarios and customers whereas computation time of the multiple deadline approach is strongly influenced by the number of deadlines and scenarios. Neither of the approaches can be described as superior to the other as both react differently to input data. Furthermore, the results show that for a large-scale implementation both approaches must be improved in their complexity to ensure a continuous operation.