A smart distribution toolbox for distribution system planning

Paper 1623The distribution system planner should be able to coordinate smart grid solutions in order to find cost effective expansions plans. These plans should be able to deal with new added system uncertainties from renewable production and consumers while guaranteeing power quality and availability of supply. This paper proposes a structure for distribution systems planning oriented to help the planner in deciding how to make use of smart solutions for achieving the described task. Here, the concept of a system planning toolbox is introduced and supported with a review of relevant works implementing smart solutions. These are colligated in a way that the system planner can foresee what to expect with their combined implementation. Future developments in this subject should attempt to theorize a practical algorithm in an optimization and decision making context.postprin

Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa

In order to reach a goal of universal access to modern energy services in Africa by 2030, consideration of various electricity sector pathways is required to help inform policy-makers and investors, and help guide power system design. To that end, and building on existing tools and analysis, we present several ‘high-level’, transparent, and economy-wide scenarios for the sub-Saharan African power sector to 2030. We construct these simple scenarios against the backdrop of historical trends and various interpretations of universal access. They are designed to provide the international community with an indication of the overall scale of the effort required. We find that most existing projections, using typical long-term forecasting methods for power planning, show roughly a threefold increase in installed generation capacity occurring by 2030, but more than a tenfold increase would likely be required to provide for full access – even at relatively modest levels of electricity consumption. This equates to approximately a 13% average annual growth rate, compared to a historical one (in the last two decades) of 1.7%.Energy Access, Power System Planning, Sub-Saharan Africa

Incentive-Based Expansion Planning and Reliability Enhancement Models for Smart Distribution Systems

Due to the rapid progress toward the implementation of smart grid technologies, electric power distribution systems are undergoing profound structural and operational changes. Climate concerns, a reduction in dependency on fossil fuel as a primary generation source, and the enhancement of existing networks constitute the key factors in the shift toward smart grid application, a shift that has, in fact, already led power industry stakeholders to promote more efficient network technologies and regulation. The results of these advances are encouraging with regard to the deployment and integration of small-scale power generation units, known as distributed generation units (DGs), within distribution networks. DGs are capable of contributing to the powering of the grid from distribution or even sub-distribution systems, providing both a positive effect on network performance and the least adverse impact on the environment. Smart grid deployment has also facilitated the integration of a variety of investor assets into power distribution systems, with a consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This thesis proposes a novel incentive-based distribution system planning (IDSP) model that enables an LDC and DG investors to work collaboratively for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program (BWIP) based on long-term contracts, which would encourage DG investors to integrate their projects at the specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors’ projects. The proposed model also provides the LDC with the opportunity to identify the least-cost solution among a combination of the proposed BWIP and traditional expansion options (i.e., upgrading or constructing new substations, upgrading or constructing new lines, and/or reconfiguring the system). In this way, the model facilitates the effective coordination of future LDC expansion projects with DG investors. To derive appropriate incentives for each project, the model enforces a number of economic metrics, including the internal rate of return, the profit-investment ratio, and the discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Several linearization approaches are applied in order to convert the proposed model into a mixed integer linear programming (MILP) model, which is solved using a CPLEX solver. Reliability of service in a deregulated power environment is considered a major factor in the evaluation of the performance of service providers by consumers and system regulators. Adhering to imposed obligations related to the enhancement of overall system reliability places a substantial burden on the planning engineer with respect to investigating multiple alternatives and evaluating each option from both a technical and an economical perspective. This thesis also proposes a value-based reinforcement planning model for improving system reliability while maintaining reliability metrics within allowable limits. The optimal allocation of tie lines and normally open switches is determined by this planning model, along with required capacity upgrades for substations and lines. Two hierarchical levels for system operation under contingencies, namely, the restoration process and islanding-based modes, are applied in the model. A probabilistic analytical model is proposed for computing distribution system reliability indices based on consideration of these two hierarchical operating levels and taking into account variations in system demand, DG output power, and the uncertainty associated with system components. Due to the nature and complexity of these kinds of problems, a metaheuristic technique based on a genetic algorithm (GA) is implemented for solving this model. This thesis also proposes a new iterative planning model for smart distribution systems in which system reliability is considered a primary component in the setting of incentive prices for DG owners. A new concept, called generation sufficiency for dynamic virtual zones, is introduced in the model as a means of enhancing reliability in areas that are subject to reliability issues. To avoid any contravention of operational security boundaries, DG capacity is represented by two components: normal DG operating capacity and reserve DG capacity. The MILP planning model is constructed in a GAMS environment and solved with the use of a CPLEX solver

River Basin Water Quality Management Models: A State-of-the-Art Review

With the increasing human activities within river basins, the problem of water quality management is becoming increasingly important. Quality management can be achieved through control/prevention measures that have various economic and water quality implications. To facilitate the analysis of available management options, decision models are needed which represent the many facets of the problem. Such models must be capable of adequately depicting the hydrological, chemical and biological processes occurring in the river; while incorporating social, economic and political considerations within the decision framework. Management analyses can be performed using simulation, optimization, or both, depending on the management goal and the size and type of the problem. The critical issues in a management model are the nonlinearities, uncertainties, multiple pollutant nature of waste discharges, multiple objectives, and the spatial and temporal distribution of management actions. Literature on various management models were reviewed under the headings of linear, nonlinear and dynamic programming approaches; their stochastic counterparts, and combined or miscellaneous approaches. Dynamic programming was found to be an attractive methodology which can exploit the sequential decision problem pertaining to river basin water quality problems (downstream control actions do not influence water quality upstream). DP handles discrete decision variables which represent discrete management alternatives, and it is generic in the sense that both linear and non-linear water quality models expressing the relation between emissions and ambient quality levels can be incorporated. An example problem is presented which demonstrates the application of a DP-based management model to formulate least-cost strategies for the Nitra River basin in Slovakia. However, it is hardly possible for a single model to represent all the aspects of a complex decision problem. Different types of management models (e.g. deterministic vs stochastic models) have different capabilities and limitations. The only way to compensate for the deficiencies is to perform the analysis in a sensitivity style. The necessity for sensitivity analyses is further implied due to the fact that water quality problems are rather loosely formulated with respect to the quality and economic goals

Risk-based methods for sustainable energy system planning: a review

The value of investments in renewable energy (RE) technologies has increased rapidly over the last decade as a result of political pressures to reduce carbon dioxide emissions and the policy incentives to increase the share of RE in the energy mix. As the number of RE investments increases, so does the need to measure the associated risks throughout planning, constructing and operating these technologies. This paper provides a state-of-the-art literature review of the quantitative and semi-quantitative methods that have been used to model risks and uncertainties in sustainable energy system planning and feasibility studies, including the derivation of optimal energy technology portfolios. The review finds that in quantitative methods, risks are mainly measured by means of the variance or probability density distributions of technical and economical parameters; while semi-quantitative methods such as scenario analysis and multi-criteria decision analysis (MCDA) can also address non-statistical parameters such as socio-economic factors (e.g. macro-economic trends, lack of public acceptance). Finally, untapped issues recognised in recent research approaches are discussed along with suggestions for future research

MODELING SUSTAINABILITY IN RENEWABLE ENERGY SUPPLY CHAIN SYSTEMS

This dissertation aims at modeling sustainability of renewable fuel supply chain systems against emerging challenges. In particular, the dissertation focuses on the biofuel supply chain system design, and manages to develop advanced modeling framework and corresponding solution methods in tackling challenges in sustaining biofuel supply chain systems. These challenges include: (1) to integrate \u27environmental thinking\u27 into the long-term biofuel supply chain planning; (2) to adopt multimodal transportation to mitigate seasonality in biofuel supply chain operations; (3) to provide strategies in hedging against uncertainty from conversion technology; and (4) to develop methodologies in long-term sequential planning of the biofuel supply chain under uncertainties. All models are mixed integer programs, which also involves multi-objective programming method and two-stage/multistage stochastic programming methods. In particular for the long-term sequential planning under uncertainties, to reduce the computational challenges due to the exponential expansion of the scenario tree, I also developed efficient ND-Max method which is more efficient than CPLEX and Nested Decomposition method. Through result analysis of four independent studies, it is found that the proposed modeling frameworks can effectively improve the economic performance, enhance environmental benefits and reduce risks due to systems uncertainties for the biofuel supply chain systems

A multi-criteria, long-term energy planning optimisation model with integrated on-grid and off-grid electrification – The case of Uganda

While electricity access is lowest in developing countries, the academic literature on generation expansion planning (GEP) has been informed almost exclusively by challenges in industrialised countries. This paper presents the first multi-objective, long-term energy planning optimisation model tailored towards national power systems with little existing power infrastructure. It determines the location, type, capacity and timing of power system infrastructure additions. Specifically, three novel generalisations of standard generation planning are introduced: (1) an expansion of the demand constraints to allow for industrial and household electrification rates below 100%, (2) a minimisation of sub-national energy access inequality in conjunction with minimising system costs considering environmental constraints, and (3) an integration of distribution infrastructure, explicitly including both on-grid and off-grid electrification. Using a specifically designed solution algorithm based on the ε-constraint method, the model was successfully applied to the previously unexplored Ugandan national power system case. The results suggest that while it is cost-optimal to maintain highly unequal sub-national access patterns to meet Uganda's official 80% electrification target in 2040, equal access rates across all districts can be achieved by increasing discounted system cost by only 3%. High optimal shares of locationally flexible on-grid and off-grid solar energy enable cheap sub-national shifts of generation capapcity. This paper strongly challenges the Ugandan government's nuclear energy and largely grid-based electrification expansion plans. Instead, it calls for solar concentrated power as a baseload option in the future and a focus on off-grid electrification which the model selects for the majority of household connections in 2040, even in a high-demand scenario.</p

Incorporating distributed generation into distribution network planning: the challenges and opportunities for distribution network operators

Diversification of the energy mix is one of the main challenges in the energy agenda of governments worldwide. Technology advances together with environmental concerns have paved the way for the increasing integration of Distributed Generation (DG) seen over recent years. Combined heat and power and renewable technologies are being encouraged and their penetration in distribution networks is increasing. This scenario presents Distribution Network Operators (DNOs) with several technical challenges in order to properly accommodate DG developments. However, depending on various factors, such as location, size, technology and robustness of the network, DG might also be beneficial to DNOs. In this thesis, the impact of DG on network planning is analysed and the implications for DNOs in incorporating DG within the network planning process are identified. In the first part, various impacts of DG to the network, such as network thermal capacity release, security of supply and on voltage, are quantified through network planning by using a modified successive elimination method and voltage sensitivity analysis. The results would potentially assist DNOs in assessing the possibilities and effort required to utilise privately-owned DG to improve network efficiency and save investment. The quantified values would also act as a fundamental element in deriving effective distribution network charging schemes. In the second part, a novel balanced genetic algorithm is introduced as an efficient means of tackling the problem of optimum network planning considering future uncertainties. The approach is used to analyse the possibilities, potential benefits and challenges to strategic network planning by considering the presence of DG in the future when the characteristics of DG are uncertain