Do smart grids make sense?

Alan Birch, Daniel Grote and Katrin Spanka describe a project to evaluate the economics of smart grids – considered essential to support changing patterns of energy generation and consumption.

The highly industrialised European economy requires good quality power supplies – and reliability. Distribution system operators (DSOs) are incentivised to provide this service in a cost-efficient manner within the relevant regulatory framework.

At the same time, integration of distributed generation and other technologies such as electric vehicles or energy storage pose challenges to the traditional business and operational processes of DSOs. Smart distribution grids are becoming enablers for endeavours to support changing patterns of power generation and consumption in Europe.

An enhanced methodology is necessary to identify cost-effective solutions from a DSO perspective. Such a methodology has been applied within a collaborative research and development project – funded under the European Commission’s seventh framework programme – called Discern, the distributed intelligence for cost-effective and reliable distribution network operation. Discern describes and implements different network solutions to aid future network development (smart grid use cases), and evaluates their technical impact and economic viability.

The smart grid use cases applied within Discern have been designed to address a number of common emerging network issues such as optimum medium-voltage network monitoring and automation, real-time monitoring of low-voltage grids, optimised automated meter reading through virtual data concentration, and identification of technical and non-technical losses.

Discern has developed a suite of practical tools that support DSOs at all stages of smart grid system design: frameworks that help DSOs develop and express the detailed requirements and architectures of their smart grid solutions, tools that promote interoperability at the semantic level, simulation and optimisation techniques for determining the optimal intelligence in networks, and methods to evaluate the technical and economic performance of a solution.

The experiences from Discern provide valuable recommendations to stakeholders that will help promote the efficient development of effective and replicable smart grid solutions, including enhancements to regulatory environments and measures that will promote the adoption of standards.

The methodology to evaluate the economic attractiveness of a use case is based on key performance indicators (KPIs) that were defined at the beginning of Discern, with data provided by DSOs from field measurements and/or through simulations. For each use case, performance KPIs were specified to measure technical impact and cost KPIs to provide information on budget and cost structure. These have been supplemented by supporting data that was necessary to use the KPI results in an economic evaluation model using the discounted cash flow method. The data included parameters to define the scope of the model, parameters to monetise performance KPIs, and parameters to detail costs, such as life expectancy of hardware/software, cost degradation, opex and communication expenditure.

Revenues are calculated by combining performance KPIs and monetary values; expenditures are calculated by combining cost KPIs, cost parameters and information on the associated equipment deployed at the demo site – from the project characteristics. Revenues and expenditures are calculated as an annual value for the start year and carried forward in consideration of supporting data for 20 years. If available, operational effects, such as measurable effects of smart grid solutions on operational processes, are accommodated. As a result, the development of the (discounted) cash flow over time and the resulting net present value (NPV) can be calculated. This provides key information on economic viability, cost-benefit ratio or payback period of the use case. Also, to account for project risk, the economic evaluation has been complemented with Monte Carlo simulation to assess the project uncertainties, and sensitivity analysis to provide insight into the sensitivity of the NPV with respect to marginal changes of input parameters. However, this approach does not yet incorporate the impact of the regulatory framework under which the use case is deployed. Only a qualitative evaluation of the impact of regulation on the net benefits is incorporated in the evaluation, with a focus on the economic regulation of the DSO’s costs and revenues.

The method is a robust initial approach to evaluate the economic attractiveness of use cases based on KPIs. However, some considerations must be taken into account when evaluating economic results:

• With regard to innovation projects, it must be acknowledged that costs may not be representative of the solution as eventually rolled out across an organisation’s network. For example, procurement may be on an overall project basis rather than individual component basis. Similarly, costs may not be representative of future “business as usual” purchase prices because these may be influenced by a variety of parameters, including the project procurement framework, the technological readiness of the devices and systems, and future developments and economies of scale.

• When evaluating R&D projects, economic results should be used as a means to identify relevant drivers of revenues and expenditures to optimise the use case for further deployment and rollout across a business.

• Economic results should be updated periodically to reflect new benefits or cost information that will influence the cost-benefit ratio or improve the accuracy of the results (particularly for assessment of the impacts on operational processes).

• Beyond the differences in costs for a DSO rolling out an innovative solution across its network, the economic results depend on the context of the use case – the characteristics and topology of the network where the use case has been implemented, the existing operational processes, the architectural fit of selected use cases to existing equipment and the level of smart grid functionality already deployed in the network, the DSO’s procurement systems and internal cost structures, and the regulatory framework.

The approach developed through Discern indicates that economic analysis of smart grid use cases provides significant value by helping identify which aspects of the developed solution and technology have the greatest impact (positive or negative) on the derived costs and benefits. This is especially useful when scaling the solution across a business or when planning to replicate the solution in other network environments.

From the initial work undertaken within Discern, we can conclude that the consideration of potential developments through time and further analysis and appraisal based on increasing experience are necessary to fully explore and describe the benefits provided by smart grid solutions. This experience will also further assist the development of sophisticated economic models to support assessment of the potential benefits and replicability of solutions.

Alan Birch, principal consultant, networks, market and strategy; Daniel Grote, senior consultant, policy and regulation; Katrin Spanka, consultant, energy business processes, DNV GL – Energy