Dynamic Pricing Tariffs Research Articles

A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems

With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted.

Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes

All-electric buildings are playing an important role in the electrification plan towards energy-neutral smart cities. Batteries are key components in all-electric buildings that can help the demand-side energy management as a flexibility asset and improve the building survivability in the case of power outages as an active survivability asset. This paper introduces a novel methodology and indexes for determining cost-effective battery sizes. It also explores the possible trade-off between energy flexibility and the survivability of all-electric buildings. The introduced methodology uses IDA-ICE 4.8 as a building performance simulation tool and MATLAB® 2017 as a post-processing calculation tool for quantifying building energy flexibility and survivability indexes. The proposed methodology is applied to a case study of a Norwegian single-family house, where 10 competitive designs, 16 uncertainty scenarios, and 3 dynamic pricing tariffs suggested by the Norwegian regulators are investigated. The methodology provides informative support for different stakeholders to compare various building designs and dynamic pricing tariffs from the flexibility and survivability points of view. Overall, the results indicate that larger cost-effective batteries usually have higher active survivability and lower energy flexibility from cost- effectiveness perspective. For instance, when the time of use tariff is applied, the cost-effective battery size varies between 40 and 65 kWh (daily storage). This is associated with a cost-effective flexibility index of 0.4–0.55%/kWh and an active survivability index of 63–80%.

A Decentralized Three-Level Optimization Scheme for Optimal Planning of a Prosumer Nano-Grid

Rapid improvement in efficiency of renewable energy sources (RESs), increased concerns about carbon emission, substantial power-flow losses, and the emergence of smart grid have all contributed to increased interest toward customers’ contribution in electricity generation in RES-based prosumer nano-grids (PNGs). Such contributions within a PNG can be effectively controlled using dynamic pricing tariffs (DPTs) that hold customers accountable for their electric power exchange behaviors. Prosumer customers optimize their internal power grid based on utility-provided DPT and expect to surpass the break-even point within an anticipated period of time. However, their expected payback period may not be met, as the DPT changes over time as more customers join the market. Therefore, the joint design of PNG and DPT is necessary to give the customers the opportunity to revisit their investment plans, and, at the same time, the utility the opportunity to adjust the pricing scheme. We propose an iterative decentralized three-level optimization scheme to achieve both objectives. The proposed scheme can be applied to a large system for planning of optimal PNGs, while providing a reliable payback period on investment for customers that are involved. The effectiveness of the proposed scheme is verified numerically using a large historical data set.

Methodology to assess business models of dynamic pricing tariffs in all-electric houses

There is a need for methodologies that integrate energy simulation and cost calculation to assess grid rent business models as incentive for demand-side management (DSM) in buildings. Despite the proliferation of energy simulation and cost calculation tools, there are no tool (e.g., software program) with appropriate methodology that caters specifically for the assessment of business models based on aggregation of dynamic pricing tariffs. Furthermore, the majority of existing methodologies focus on evaluating the supply-side management (SSM) of energy grids, and largely overlook the issue of influencing the customer to make good choices when it comes to DSM and/or design/renovation actions. This paper introduces energy and cost oriented methodology that provides informative support for utility companies and electric-grid customers including households’ occupants to assess the economic incentives of different energy and power dynamic pricing tariffs. A physical model-based building simulation tool (IDA-ICE) is used to assess the energy performance of a representative residential benchmark including 96 all-electric houses in Norway with and without renewable energy technology. A business model-based cost calculator is developed and linked with the energy simulation's outputs to assess the effectiveness of three dynamic pricing tariffs, suggested recently by the Norwegian Water Resources and Energy Directorate (NVE). The effectiveness of the three pricing tariffs is compared (improving building's energy efficiency vs enhancing grid's demand side load shifting). Overall, results indicate that the Tiered Rate tariff is the most effective business strategy for customers to reduce the electric-based heating load during high demand periods. However, the methodology generated a comprehensive suite of scenarios analysis that allow customers, utility companies and policy makers to accurately address several building renovation variations and demand side management strategies to make the right decision upfront.

Stackelberg game‐based energy management for a microgrid with commercial buildings considering correlated weather uncertainties

This paper proposes a Stackelberg game-based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi-level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush–Kuhn–Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation-based 2 M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method.

Industrial Consumers’ Smart Grid Adoption: Influential Factors and Participation Phases

The participation of industrial consumers in smart grid transition is important due to their consumption footprint, heavy energy use and complexity in the implementation of smart energy technologies. Active involvement of industrial consumers in the development of smart grid solutions is important to ensure the energy system transformation. Despite the importance of industrial consumers has been identified, the empirical studies on the smart grid still mainly address residential and commercial consumers. Therefore, based on four case studies with two industrial consumers, one energy consulting company and one electricity retailer, this paper investigates the factors that influence industrial consumers’ acceptance of smart grid solutions, and how the influential factors are relevant to the smart grid adoption phases. Eleven influential factors are identified that impact on four stages for industrial consumers’ adoption of smart grid solutions (inscription, translation, framing, and stabilization stages). The eleven influential factors are: awareness of multiple contexts, shared support, return-of-investment, ease of use, flexibility and dynamic pricing, liberalization and energy tariff structure, customer focus, solution integration, process improvement, service quality, and company’s green image.

Control of Energy Storage

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...]

Coordinated Operation of a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed Generation

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

Dynamic Pricing Design for Demand Response Integration in Power Distribution Networks

This paper presents optimal pricing design for demand response (DR) integration in the distribution network. In particular, we study the energy scheduling problem for a load serving entity (LSE) that serves two types of loads, namely inflexible and flexible loads. Inflexible loads are charged under a regular pricing tariff while flexible loads enjoy a dynamic pricing tariff that ensures cost saving for them. Moreover, flexible loads are assumed to be aggregated by several DR aggregators. The interaction between the LSE and its customers is formulated as a bilevel optimization problem where the LSE is the leader and DR aggregators are the followers. The optimal solution of this problem corresponds to the optimal pricing tariff for flexible loads. The key advantage of the proposed model is that it can be readily implemented thanks to its compatibility with existing pricing structures in the retail market. Extensive numerical results show that the proposed approach provides a win-win solution for both the LSE and its customers.

A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility’s consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system.

Self-Learning Load Characteristic Models for Smart Appliances

It is generally accepted that if dynamic electricity pricing tariffs were to be introduced, their effectiveness in controlling domestic loads will be curtailed if consumers were relied on to respond in their own interests. The complexities of relating behavior to load to price are so burdensome that at least some degree of automation would be required to take advantage of pricing signals. However, a major issue with home automation is fitting in with the lifestyles of individual consumers. Truly smart appliances that can learn the details of their routine operation may be several years away from widespread adoption making integrated home energy management systems unfeasible. Similarly, usage patterns of these same appliances may be substantially different from household to household. The contribution of this paper is the proposal and demonstration of a set of probabilistic models that act in a framework to reduce appliance usage data into contextual knowledge that accounts for variability in patterns in usage. Using sub-metered load data from various domestic wet appliances, the proposed technique is demonstrated learning the appliance operating likelihood surfaces from no prior knowledge.

Integrating Home Energy Simulation and Dynamic Electricity Price for Demand Response Study

This paper studies how to develop and evaluate demand response strategies from the consumer's perspective through a computational experiment approach. The proposed approach includes a home energy consumption simulator, a demand response mechanism obtained through optimization, particle swam or heuristic method, and an integrative computing platform that combines the home energy simulator and MATLAB together for demand response development and evaluation. Several demand response strategies are developed and evaluated through the computational experiment technique. The paper investigates and compares characteristics of different demand response strategies and how they are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

Dynamic pricing of electricity in the mid-Atlantic region: econometric results from the Baltimore gas and electric company experiment

The Baltimore Gas and Electric Company (BGE) undertook a dynamic pricing experiment to test customer price responsiveness to different dynamic pricing options. The pilot ran during the summers of 2008 and 2009 and was called the Smart Energy Pricing (SEP) Pilot. In 2008, it tested two types of dynamic pricing tariffs: critical peak pricing (CPP) and peak time rebate (PTR) tariffs. About a thousand customers were randomly placed on these tariffs and some of them were paired with one of two enabling technologies, a device known as the Energy Orb and a switch for cycling central air conditioners. The usage of a randomly chosen control group of customers was also monitored during the same time period. In 2009, BGE repeated the pilot program with the same customers who participated in the 2008 pilot, but this time it only tested the PTR tariff. In this paper, we estimate a constant elasticity of substitution (CES) model on the SEP pilot’s hourly consumption, pricing and weather data. We derive substitution and daily price elasticities and predictive equations for estimating the magnitude of demand response under a variety of dynamic prices. We also test for the persistence of impacts across the two summers. In addition, we report average peak demand reduction for each of the treatment cells in the SEP pilot and compare the findings with those reported from earlier pilots. These results show conclusively that it is possible to incentivize customers to reduce their peak period loads using price signals. More importantly, these reductions do not wear off when the pricing plans are implemented over two consecutive summers. Our analyses reveal that SEP participants reduced their peak usages in the range of 18 to 33% in the first summer of the SEP pilot and continued these reductions in the second summer.

The Econometrics of Dynamic Pricing in a Mid-Atlantic Experiment

The Baltimore Gas & Electric Company (BGE) undertook a dynamic pricing experiment to test customer price responsiveness to different dynamic pricing options. The pilot ran during the summers of 2008 and 2009 and was called the Smart Energy Pricing (SEP) Pilot. In 2008, it tested two types of dynamic pricing tariffs: critical peak pricing (CPP) and peak time rebate (PTR) tariffs. Some thousand customers who randomly placed on these tariffs and some of them were paired with one of two enabling technologies, a device known as the Energy Orb and a switch for cycling central air conditioners. The usage of a randomly chosen control group of customers was also monitored during the same time period. In 2009, the pilot only tested the PTR tariff. In this paper, we estimate a well-known demand model on the hourly consumption, pricing and weather data that was compiled in the SEP pilot. We derive substitution and daily price elasticities and predictive equations for estimating the magnitude of demand response under a variety of dynamic prices. We also test for the persistence of impacts across the two summers. We also report average peak demand reduction for each of the treatment cells in the SEP pilot and compare the findings with those reported from earlier pilots.

Transitioning to Dynamic Pricing

With the advent of the smart grid, dynamic pricing is receiving increasing interest by state commissions throughout North America as a means of enhancing economic efficiency by reducing the need for expensive peaking capacity. But several barriers stand in the way of its rapid deployment. As noted by MIT's Paul Joskow in a recent discussion of the economics of climate change, On the demand side there are relatively low cost ways to reduce electricity consumption by increasing energy efficiency in building, lighting, heating, ventilating, air conditioning and other equipment. That's why getting the retail price signals right is important and why muting them with regulation based on traditional cost of service models is inconsistent with promoting adoption of economical energy efficiency opportunities. While the rate design process (in conjunction with the revenue requirements process) in principle results in utility recovery of all prudent costs, it provide sufficient incentives to utilities to pursue energy efficiency and demand response programs at a level commensurate with state and federal goals. A review of default rate designs across the continent reveals that prices paid by customers do not reflect the scarcity of capacity to produce energy at various times of day. There is a lack of recognition that the default rates embody a hedging or risk premium which insulates customers from price volatility and eliminates any incentive that they would otherwise have for moving to dynamic pricing tariffs. In addition, customers lack the information to become smart shoppers. Policy makers have bought into a viewpoint espoused by defenders of the status quo that customers are averse to being placed on dynamic pricing tariffs, since not only will they face price volatility but they may also pay higher bills. This is contradicted by evidence from fifteen recent pilots with dynamic pricing, which clearly showed that once customers experienced a dynamic tariff, not only did they understand and respond to the price signals, they also overwhelming preferred dynamic tariffs to their conventional hedged rate form. The experiments also showed that a well-thought out customer education program is needed to sustain customer response. In order to make a transition to dynamic pricing, a new framework is needed to develop innovative rate designs.

The Summer of 2006: A Milestone in the Ongoing Maturation of Demand Response

How did DR programs perform in 2006? For reliability programs, the resounding answer from industry participants was “very well.” However, the performance of economic DR programs and dynamic pricing tariffs received somewhat less glowing reports.