Real-time peak-demand minimization with energy storage using competitive ratio

The high proportions of demand charges in electric bills motivate large-power customers to leverage energy storage for reducing the peak procurement from the outer grid. Given limited energy storage, we expect to maximize the peak-demand reduction in an online fashion, challenged by the highly uncertain demands and renewable injections, the non-cumulative nature of peak consumption, and the coupling of online decisions. In this paper, we propose an optimal online algorithm that achieves the best competitive ratio, following the idea of maintaining a constant ratio between the online and the optimal offline peak-reduction performance. We further show that the optimal competitive ratio can be computed by solving a linear number of linear-fractional programs. Moreover, we extend the algorithm to adaptively maintain the best competitive ratio given the revealed inputs and actions at each decision-making round. The adaptive algorithm retains the optimal worst-case guarantee and attains improved average-case performance. We evaluate our proposed algorithms using real-world traces and show that they obtain up to 81% peak reduction of the optimal offline benchmark. Additionally, the adaptive algorithm achieves at least 20% more peak reduction against baseline alternatives.

The peak-demand charge motivates large-load customers to flatten their demand curves, while their self-owned renewable generations aggravate demand fluctuations. Thus, it is attractive to utilize energy storage for shaping real-time loads and reducing electricity bills. In this paper, we propose the first peak-aware competitive online algorithm for leveraging stored energy (e.g., in fuel cells) to minimize peak-demand charges. Our algorithm decides the discharging quantity slot by slot to maintain the optimal worst-case performance guarantee (namely, competitive ratio) among all deterministic online algorithms. Interestingly, we show that the best competitive ratio can be computed by solving a linear number of linear-fractional problems. We can also extend our competitive algorithm and analysis to improve the average-case performance and consider short-term prediction.

In deregulated energy markets, consumers -ranging from households to data centers-have access to multiple offers, often through multiple suppliers and energy carriers (i.e. electric, thermal) or through local generation, such as renewable energy sources and energy storage. Ideally, supply should match demand, leading to a balanced power grid, but this is challenging in practice: while some generation sources can be planned in advance (e.g. utility offers), others can be planned to a limited degree or cannot be planned altogether (e.g. storage and renewable energy sources respectively). In this context, we focus on how to address systematically this complex resource allocation problem in the presence of multiple actors. In this work, utilizing a proposed modeling of the energy dispatch problem as an online scheduling problem, we model supply-following demand in terms of the Adwords problem, in order to provide algorithmic solutions of measurable quality. Building on earlier work in the literature, we extend the Adwords problem to incorporate load credit (i.e. storage) and we present and analyze online algorithms that can schedule demand, given availability constraints on supply, with guaranteed competitive ratio. In systems where individual demands are small compared to supply, we prove a (1 - 1/e) -competitive ratio. We also extend the Adwords problem to utilize dynamic budgets, with application in cases where the above assumption does not hold, and present an algorithm with a 1/2-competitive ratio. We also provide examples of algorithmic performance in real world scenarios, by utilizing long term, fine-grained data from a pilot project in Sweden by a major utility company and in collaboration with the local government authority, while taking into account renewable generation on site.

The fluctuations of electricity prices in demand response schemes and intermittency of renewable energy supplies necessitate the adoption of energy storage in microgrids. However, it is challenging to design effective real-time energy storage management strategies that can deliver assured optimality, without being hampered by the uncertainty of volatile electricity prices and renewable energy supplies. This paper presents a simple effective online algorithm for the charging and discharging decisions of energy storage that minimizes the electricity cost in the presence of electricity price fluctuations and renewable energy supplies, without relying on the future information of prices, demands or renewable energy supplies. The proposed algorithm is supported by a near-best worst-case guarantee (i.e., competitive ratio), as compared to the offline optimal decisions based on full future information. Furthermore, the algorithm can be adapted to take advantage of limited future information, if available. By simulations on real-world data, it is observed that the proposed algorithms can achieve satisfactory outcome in practice.

Renewable and distributed energy sources are today possible but these technologies bring benefits as well as challenges, such as their intermittent nature, that leads to utilization problems for the power grid. On the other hand, upcoming storage technologies, such as electric vehicles, hold the potential to store and utilize this intermittent supply at a later time but bring challenges of their own, for example efficient storage utilization and intermittent energy demand. In this paper we propose a novel modelling of the problem of unforecasted energy dispatch with storage as an online scheduling problem of tasks on machines, by transforming time constraints of energy requests into equivalent machine constraints as well as by modelling energy storage through the extension of existing online scheduling techniques with the concept of load credit. Based on this transformation, we also present an algorithm that dispatches load and utilizes efficiently any storage capabilities in order to mitigate the effect of unreliable or non-existent demand forecasts, and we prove that the resulting solution's competitive ratio is within a logarithmic factor of the optimal offline solution. Finally, we provide an extensive simulation study for a variety of scenarios based on data from a large network of consumers, showing that the presented algorithm is highly competitive even to methods that assume exact knowledge about the demand requests.

This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.

Competitive bidding strategies for online linear optimization with inventory management constraints

Reductions in energy consumption, carbon footprint, equipment size, and cost are key objectives for the forthcoming energy-intensive industries roadmaps. In this sense, solutions such as waste heat recovery, which can be replicated into different sectors (e.g., ceramics, concrete, glass, steel, aluminium, pulp, and paper) are highly promoted. In this line, latent heat thermal energy storage (TES) contributes as an innovative technology solution to improve the overall system efficiency by recovering and storing industrial waste heat. To this end, phase-change material (PCM) selection is assisted through a decision-support system (DSS). A simplified tool based on the MATLAB® model, based on correlations among the most relevant system parameters, was developed to prove the feasibility of a cross-sectorial approach. The research work conducted a parametric analysis to assess the techno-economic performance of the PCM-TES solution under different working conditions and sectors. Additionally, a multicriteria assessment was performed comparing the tool outputs from metal alloys and inorganic hydrated PCM salts. Overall, the inorganic PCMs presented higher net economic and energy savings (up to 25,000 €/yr; 480 MWh/yr), while metal alloys involved promising results, shorter cycles, and competitive economic ratios; its commercial development is still limited.

This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her time-varying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers.

This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her timevarying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers.

This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her timevarying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers.

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the mobile network edge. This provides end users with swift and powerful computing and storage capacities, energy efficiency, and mobility- and context-awareness support. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. However, user requests arrive dynamically and different users demand different amounts of resources, while the resources in MEC are dynamically occupied or released by different services. It thus poses a significant challenge to optimize the performance of MEC through efficient computing and communication resource allocations to meet ever-growing resource demands of users. In this paper, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. Specifically, we first devise an approximation algorithm for the cost minimization problem of admitting a single NFV-enabled multicast request. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We third devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the edge of core networks. This provides users with powerful computing and storage capacities, energy efficiency, and mobility—and context-aware supporting. Multicasting in MEC is a fundamental functionality of many network applications of mobile users, including online conferencing, event monitoring, video streaming, and so on. To guarantee the security and privacy of each multicast traffic session, a service chain that consists of security network functions usually is associated with each multicast request to process its traffic. In this chapter, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network. We first devise approximation algorithms for the cost minimization problem of admitting a single NFV-enabled multicast request, by assuming that the virtualized network functions may or may not be consolidated into a single location. We then devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.KeywordsNFV-enabled multicastingMobile edge computingNetwork function virtualizationApproximation and online algorithms

This paper studies the problem of utilizing heterogeneous energy storage systems, including electric vehicles and residential batteries, to perform demand-response in microgrids. The objective is to minimize the operational cost while fulfilling the demand-response requirement. The design space is to select and schedule a subset of available storage devices that are heterogeneous in operating cost, capacity, and availability in time. Designing a performance-optimized solution, however, is challenging due to the combinatorial nature of the problem with mixed packing and covering constraints, and the essential need for online solution design in practical scenarios where both demand-response requirement and the profile of user-owned storage systems arrive online. We tackle these challenges and design several online algorithms by leveraging a recent theoretical computer science technique which uses a problem-specific exponential potential function to solve online mixed packing and covering problems. We show that the fractional version of the algorithm achieves a logarithmic bi-criteria competitive ratio. Empirical trace-driven experiments demonstrate that our algorithms perform much better than the theoretical bounds and achieve close-to-optimal performance.

The co-existence of small cell base stations (SBSs) with conventional macrocell base station is a promising approach to boost the capacity and coverage of cellular networks. However, densifying the network with a viral deployment of SBSs can significantly increase energy consumption. To reduce the reliance on unsustainable energy sources, one can adopt self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly optimize their ON and OFF schedule. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisted of energy consumption and transmission delay of a network. For the original problem, we show an algorithm can solve the problem in the illustrative case. To reduce the complexity of the original problem, an approximation is proposed. To solve the approximated problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. Using this approach, each SBS can effectively decide on its ON/OFF schedule autonomously, without any prior information on future energy arrivals. By using competitive analysis, a deterministic online algorithm (DOA) and a randomized online algorithm (ROA) are developed. ROA is shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared to a baseline approach, the ROA can yield performance gains reaching up to 15.6% in terms of reduced total energy consumption of SBSs and up to 20.6% in terms of per-SBS network delay reduction. The results shed light on the fundamental aspects that impact the ON time of SBSs while demonstrating that the proposed ROA can reduce up to 69.9% the total cost compared to a baseline approach.