Electric Vehicle Storage Management in Operating Reserve Auctions

Carsharing operators, which rent out electric vehicles for minutes or hours, lose money on idle vehicles. We develop a model that allows carsharing operators to offer the storage of these vehicles on operating reserve markets (market for quickly rampable back-up power sources that replace for instance failing power plants). We consider it a dispatch and pricing problem with the tradeoff between the payoffs of offering vehicles for rental and selling their storage. This is a problem of stochastic nature taking into account that people can rent electric vehicles at any time. To evaluate our model we tracked the location and status of 350 electric vehicles from the carsharing company Car2Go and simulated the dispatch in the Dutch market. This market needs to be redesigned for optimal use of storage. We make recommendations for the market redesign and show that carsharing operators can make substantial additional profits in operating reserve markets

Multi Agent Coordination for Demand Management with Energy Generation and Storage

In this paper, we focus on demand side management in consumer collectives with community owned renewable energy generation and storage facilities for effective integration of renewable energy with the existing fossil fuelbased power supply system. The collective buys energy as a group through a central coordinator who also decides about the storage and usage of renewable energy. produced by the collective. Our objective is to design coordination algorithms to minimize the cost of electricity consumption of the consumer collective while allowing the consumers to make their own consumption decisions based on their private consumption constraints and preferences. Minimizing the cost is not only of interest to the consumers but is also socially desirable because it reduces the consumption at times of peak demand (since differential pricing mechanisms like time-of-use pricing is usually used by electricity companies to discourage consumption at times of peak demand). We develop an iterative coordination algorithm in which the coordinator makes the storage decision and shapes the demands of the consumers by designing a virtual price signal for the agents. We prove that our algorithm converges, and it achieves the optimal solution under realistic conditions We also present simulation results based on real world consumption data to quantify the performance of our algorithm

Distributed Control of Micro-Storage Devices With Mean Field Games

This paper proposes a fully distributed control strategy for the management of micro-storage devices that perform energy arbitrage. For large storage populations, the problem can be approximated as a differential game with infinite players (mean field game). Through the resolution of coupled partial differential equations (PDEs), it is possible to determine, as a fixed point, the optimal feedback strategy for each player and the resulting price of energy if that strategy is applied. Once this price is calculated, it can be communicated to the devices, which are able to independently determine their optimal charge profile. Simulation results are provided, calculating the fixed point through numerical integration of the PDEs. The original model is then extended in order to consider additional elements, such as multiple population of devices and demand uncertainty

The Value of IS-Enabled Flexibility in Electricity Demand - a Real Options Approach

As the transition to renewable energy sources progresses, their integration makes electricity production increasingly fluctuating, also causing amplified volatility in electricity prices on energy markets. To contribute to power grid stability, utilities need to balance volatile supply through shifting demand. This measure of demand side management creates flexibility, being enabled as the integration of IS in the power grid grows. The flexibility of deferring consumption to times of lower demand or higher supply bears an economic value. We show how to quantify this value in order to support decisions on short-term consumer compensation. We adapt real options theory, which has been widely used in IS research for valuation under uncertainty. Addressing a prerequisite, we develop a stochastic process, which realistically replicates intraday electricity spot price development. We employ it in a binomial tree model to assess the value of IS-enabled flexibility in electricity demand

Effective Consumption Scheduling for Demand-Side Management in the Smart Grid using Non-Uniform Participation Rate

Periods of peak consumer demand in today’s electricity sector are expensive to satisfy and can be the source of power failures. One possible solution is the use of demand-side management (DSM) applying dynamic pricing mechanisms. However, instead of reducing peak loads, these mechanisms can lead to peak-shifting due to the herding effect of consumers’ load-shifting behavior. To overcome this problem, we explore strategies of assigning (non-uniform) participation rates to consumers. We use a generic method to find a near-optimal distribution setting for participation rates. Our method allows DSM designers to tune the system toward consumer convenience. This means less frequent consumption schedule changes, in the price of system performance. In addition, consumers do not need to reveal their detailed consumption schedules (hence, their privacy is preserved). Using experiments, we show the impact of the herding effect and evaluate the effectiveness of the proposed solution. We thereby demonstrate price fairness for consumers. Finally, we apply our solution to a more realistic environment – one where consumers change their consumption behavior every day

When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders’ profit. Using our customer models, we successfully show how customer participation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes

Providing Utility to Utilities: The Value of Information Systems Enabled Flexibility in Electricity Consumption

As the transition to renewable energy sources progresses, the integration of such sources makes electricity production increasingly fluctuate. To contribute to power grid stability, electric utilities must balance volatile supply by shifting demand. This measure of demand response depends on flexibility, which arises as the integration of information systems in the power grid grows. The option to shift electric loads to times of lower demand or higher supply bears an economic value. Following a design science research approach, we illustrate how to quantify this value to support decisions on short-term consumer compensation. We adapt real options theory to the design—a strategy that IS researchers have used widely to determine value under uncertainty. As a prerequisite, we develop a stochastic process, which realistically replicates intraday electricity spot price development. With this process, we design an artifact suitable for valuation, which we illustrate in a plug-in electric vehicle scenario. Following the artifact’s evaluation based on historical spot price data from the electricity exchange EPEX SPOT, we found that real options analysis works well for quantifying the value of information systems enabled flexibility in electricity consumption

Enabling cooperative and negotiated energy exchange in remote communities

Energy poverty at the household level is defined as the lack of access to electricity and reliance on the traditional use of biomass for cooking, and is a serious hindrance to economic and social development. It is estimated that 1.3 billion people live without access to electricity and almost 2.7 billion people rely on biomass for cooking, a majority of whom live in small communities scattered over vast areas of land (mostly in the Sub-Saharan Africa and the developing Asia). Access to electricity is a serious issue as a number of socio-economic factors, from health to education, rely heavily on electricity. Recent initiatives have sought to provide these remote communities with off-grid renewable microgeneration infrastructure such as solar panels, and electric batteries. At present, these resources (i.e., microgeneration and storage) are operated in isolation for individual home needs, which results in an inefficient and costly use of resources, especially in the case of electric batteries which are expensive and have a limited number of charging cycles. We envision that by connecting homes together in a remote community and enabling energy exchange between them, this microgeneration infrastructure can be used more efficiently. Against this background, in this thesis we investigate the methods and processes through which homes in a remote community can exchange energy. We note that remote communities lack general infrastructure such as power supply systems (e.g., the electricity grid) or communication networks (e.g., the internet), that is taken for granted in urban areas. Taking these challenges into account and using insights from knowledge domains such game theory and multi-agent systems, we present two solutions: (i) a cooperative energy exchange solution and (ii) a negotiated energy exchange solution, in order to enable energy exchange in remote communities.Our cooperative energy exchange solution enables connected homes in a remote community to form a coalition and exchange energy. We show that such coalition a results in two surpluses: (i) reduction in the overall battery usage and (ii) reduction in the energy storage losses. Each agents's contribution to the coalition is calculated by its Shapley value or, by its approximated Shapley value in case of large communities. Using real world data, we empirically evaluate our solution to show that energy exchange: (i) can reduce the need for battery charging (by close to 65%) in a community; compared with when they do not exchange energy, and (ii) can improve the efficient use of energy (by up to 10% under certain conditions) compared with no energy exchange. Our negotiated energy exchange solution enables agents to negotiate directly with each other and reach energy exchange agreements. Negotiation over energy exchange is an interdependent multi-issue type of negotiation that is regarded as very difficult and complex. We present a negotiation protocol, named Energy Exchange Protocol (EEP), which simplifies this negotiation by restricting the offers that agents can make to each other. These restrictions are engineered such that agents, negotiation under the EEP, have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes (within restricted the set of offers) in a decentralised manner. Using real world data, we empirically evaluate our protocol and show that, in this instance, a society of agents can: (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%, compared to when they do not exchange energy

Distributed Coordination and Optimisation of Network-Aware Electricity Prosumers

Electricity networks are undergoing a transformation brought on by new technologies, market pressures and environmental concerns. This includes a shift from large centralised generators to small-scale distributed generators. The dramatic cost reductions in rooftop solar PV and battery storage means that prosumers (houses and other entities that can both produce and consume electricity) will have a large role to play in future networks. How can networks be managed going forward so that they run as efficiently as possible in this new prosumer paradigm? Our vision is to treat prosumers as active participants by developing a mechanism that incentivises them to help balance power and support the network. The whole process is automated to produce a near-optimal outcome and to reduce the need for human involvement. The first step is to design an autonomous energy management system (EMS) that can optimise the local costs of each prosumer in response to network electricity prices. In particular, we investigate different optimisation strategies for an EMS in an uncertain household environment. We find that the uncertainty associated with weather, network pricing and occupant behaviour can be effectively handled using online optimisation techniques using a forward receding horizon. The next step is to coordinate the actions of many EMSs spread out across the network, in order to minimise the overall cost of supplying electricity. We propose a distributed algorithm that can efficiently coordinate a network with thousands of prosumers without violating their privacy. We experiment with a range of power flow models of varying degrees of accuracy in order to test their convergence rate, computational burden and solution quality on a suburb-sized microgrid. We find that the higher accuracy model, although non-convex, converges in a timely manner and produces near-optimal solutions. We also develop simple but effective techniques for dealing with residential shiftable loads which require discrete decisions. The final part of the problem we explore is prosumer manipulation of the coordination mechanism. The receding horizon nature of our algorithm is great for managing uncertainty, but it opens up unique opportunities for prosumers to manipulate the actions of others. We formalise this form of receding horizon manipulation and investigate the benefits manipulative agents can obtain. We find that indeed strategic agents can harm the system, but only if they are large enough and have information about the behaviour of other agents. For the rare cases where this is possible, we develop simple privacy-preserving identifiers that monitor agents and distinguish manipulation from uncertainty. Together, these components create a complete solution for the distributed coordination and optimisation of network-aware electricity prosumers